{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "6685b77e",
   "metadata": {},
   "source": [
    "## DAY 41"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4153a4c9",
   "metadata": {},
   "source": [
    "首先回顾下昨天的代码。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "fa443a0f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# import torch\n",
    "# import torch.nn as nn\n",
    "# import torch.optim as optim\n",
    "# from torchvision import datasets, transforms\n",
    "# from torch.utils.data import DataLoader\n",
    "# import matplotlib.pyplot as plt\n",
    "# import numpy as np\n",
    "\n",
    "# # 设置中文字体支持\n",
    "# plt.rcParams[\"font.family\"] = [\"SimHei\"]\n",
    "# plt.rcParams['axes.unicode_minus'] = False  # 解决负号显示问题\n",
    "\n",
    "# # 1. 数据预处理\n",
    "# transform = transforms.Compose([\n",
    "#     transforms.ToTensor(),                # 转换为张量\n",
    "#     transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))  # 标准化处理\n",
    "# ])\n",
    "\n",
    "# # 2. 加载CIFAR-10数据集\n",
    "# train_dataset = datasets.CIFAR10(\n",
    "#     root='./data',\n",
    "#     train=True,\n",
    "#     download=True,\n",
    "#     transform=transform\n",
    "# )\n",
    "\n",
    "# test_dataset = datasets.CIFAR10(\n",
    "#     root='./data',\n",
    "#     train=False,\n",
    "#     transform=transform\n",
    "# )\n",
    "\n",
    "# # 3. 创建数据加载器\n",
    "# batch_size = 64\n",
    "# train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True)\n",
    "# test_loader = DataLoader(test_dataset, batch_size=batch_size, shuffle=False)\n",
    "\n",
    "# # 4. 定义MLP模型（适应CIFAR-10的输入尺寸）\n",
    "# class MLP(nn.Module):\n",
    "#     def __init__(self):\n",
    "#         super(MLP, self).__init__()\n",
    "#         self.flatten = nn.Flatten()  # 将3x32x32的图像展平为3072维向量\n",
    "#         self.layer1 = nn.Linear(3072, 512)  # 第一层：3072个输入，512个神经元\n",
    "#         self.relu1 = nn.ReLU()\n",
    "#         self.dropout1 = nn.Dropout(0.2)  # 添加Dropout防止过拟合\n",
    "#         self.layer2 = nn.Linear(512, 256)  # 第二层：512个输入，256个神经元\n",
    "#         self.relu2 = nn.ReLU()\n",
    "#         self.dropout2 = nn.Dropout(0.2)\n",
    "#         self.layer3 = nn.Linear(256, 10)  # 输出层：10个类别\n",
    "        \n",
    "#     def forward(self, x):\n",
    "#         # 第一步：将输入图像展平为一维向量\n",
    "#         x = self.flatten(x)  # 输入尺寸: [batch_size, 3, 32, 32] → [batch_size, 3072]\n",
    "        \n",
    "#         # 第一层全连接 + 激活 + Dropout\n",
    "#         x = self.layer1(x)   # 线性变换: [batch_size, 3072] → [batch_size, 512]\n",
    "#         x = self.relu1(x)    # 应用ReLU激活函数\n",
    "#         x = self.dropout1(x) # 训练时随机丢弃部分神经元输出\n",
    "        \n",
    "#         # 第二层全连接 + 激活 + Dropout\n",
    "#         x = self.layer2(x)   # 线性变换: [batch_size, 512] → [batch_size, 256]\n",
    "#         x = self.relu2(x)    # 应用ReLU激活函数\n",
    "#         x = self.dropout2(x) # 训练时随机丢弃部分神经元输出\n",
    "        \n",
    "#         # 第三层（输出层）全连接\n",
    "#         x = self.layer3(x)   # 线性变换: [batch_size, 256] → [batch_size, 10]\n",
    "        \n",
    "#         return x  # 返回未经过Softmax的logits\n",
    "\n",
    "# # 检查GPU是否可用\n",
    "# device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
    "\n",
    "# # 初始化模型\n",
    "# model = MLP()\n",
    "# model = model.to(device)  # 将模型移至GPU（如果可用）\n",
    "\n",
    "# criterion = nn.CrossEntropyLoss()  # 交叉熵损失函数\n",
    "# optimizer = optim.Adam(model.parameters(), lr=0.001)  # Adam优化器\n",
    "\n",
    "# # 5. 训练模型（记录每个 iteration 的损失）\n",
    "# def train(model, train_loader, test_loader, criterion, optimizer, device, epochs):\n",
    "#     model.train()  # 设置为训练模式\n",
    "    \n",
    "#     # 记录每个 iteration 的损失\n",
    "#     all_iter_losses = []  # 存储所有 batch 的损失\n",
    "#     iter_indices = []     # 存储 iteration 序号\n",
    "    \n",
    "#     for epoch in range(epochs):\n",
    "#         running_loss = 0.0\n",
    "#         correct = 0\n",
    "#         total = 0\n",
    "        \n",
    "#         for batch_idx, (data, target) in enumerate(train_loader):\n",
    "#             data, target = data.to(device), target.to(device)  # 移至GPU\n",
    "            \n",
    "#             optimizer.zero_grad()  # 梯度清零\n",
    "#             output = model(data)  # 前向传播\n",
    "#             loss = criterion(output, target)  # 计算损失\n",
    "#             loss.backward()  # 反向传播\n",
    "#             optimizer.step()  # 更新参数\n",
    "            \n",
    "#             # 记录当前 iteration 的损失\n",
    "#             iter_loss = loss.item()\n",
    "#             all_iter_losses.append(iter_loss)\n",
    "#             iter_indices.append(epoch * len(train_loader) + batch_idx + 1)\n",
    "            \n",
    "#             # 统计准确率和损失\n",
    "#             running_loss += iter_loss\n",
    "#             _, predicted = output.max(1)\n",
    "#             total += target.size(0)\n",
    "#             correct += predicted.eq(target).sum().item()\n",
    "            \n",
    "#             # 每100个批次打印一次训练信息\n",
    "#             if (batch_idx + 1) % 100 == 0:\n",
    "#                 print(f'Epoch: {epoch+1}/{epochs} | Batch: {batch_idx+1}/{len(train_loader)} '\n",
    "#                       f'| 单Batch损失: {iter_loss:.4f} | 累计平均损失: {running_loss/(batch_idx+1):.4f}')\n",
    "        \n",
    "#         # 计算当前epoch的平均训练损失和准确率\n",
    "#         epoch_train_loss = running_loss / len(train_loader)\n",
    "#         epoch_train_acc = 100. * correct / total\n",
    "        \n",
    "#         # 测试阶段\n",
    "#         model.eval()  # 设置为评估模式\n",
    "#         test_loss = 0\n",
    "#         correct_test = 0\n",
    "#         total_test = 0\n",
    "        \n",
    "#         with torch.no_grad():\n",
    "#             for data, target in test_loader:\n",
    "#                 data, target = data.to(device), target.to(device)\n",
    "#                 output = model(data)\n",
    "#                 test_loss += criterion(output, target).item()\n",
    "#                 _, predicted = output.max(1)\n",
    "#                 total_test += target.size(0)\n",
    "#                 correct_test += predicted.eq(target).sum().item()\n",
    "        \n",
    "#         epoch_test_loss = test_loss / len(test_loader)\n",
    "#         epoch_test_acc = 100. * correct_test / total_test\n",
    "        \n",
    "#         print(f'Epoch {epoch+1}/{epochs} 完成 | 训练准确率: {epoch_train_acc:.2f}% | 测试准确率: {epoch_test_acc:.2f}%')\n",
    "    \n",
    "#     # 绘制所有 iteration 的损失曲线\n",
    "#     plot_iter_losses(all_iter_losses, iter_indices)\n",
    "    \n",
    "#     return epoch_test_acc  # 返回最终测试准确率\n",
    "\n",
    "# # 6. 绘制每个 iteration 的损失曲线\n",
    "# def plot_iter_losses(losses, indices):\n",
    "#     plt.figure(figsize=(10, 4))\n",
    "#     plt.plot(indices, losses, 'b-', alpha=0.7, label='Iteration Loss')\n",
    "#     plt.xlabel('Iteration（Batch序号）')\n",
    "#     plt.ylabel('损失值')\n",
    "#     plt.title('每个 Iteration 的训练损失')\n",
    "#     plt.legend()\n",
    "#     plt.grid(True)\n",
    "#     plt.tight_layout()\n",
    "#     plt.show()\n",
    "\n",
    "# # 7. 执行训练和测试\n",
    "# epochs = 20  # 增加训练轮次以获得更好效果\n",
    "# print(\"开始训练模型...\")\n",
    "# final_accuracy = train(model, train_loader, test_loader, criterion, optimizer, device, epochs)\n",
    "# print(f\"训练完成！最终测试准确率: {final_accuracy:.2f}%\")\n",
    "\n",
    "# # # 保存模型\n",
    "# # torch.save(model.state_dict(), 'cifar10_mlp_model.pth')\n",
    "# # # print(\"模型已保存为: cifar10_mlp_model.pth\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a66f4776",
   "metadata": {},
   "source": [
    "可以看到即使在深度神经网络情况下，准确率仍旧较差，这是因为特征没有被有效提取----真正重要的是特征的提取和加工过程。MLP把所有的像素全部展平了（这是全局的信息），无法布置到局部的信息，所以引入了卷积神经网络。（在之前的复试班已经交代清楚了，如果不清楚什么是卷积神经网络，请自行学习下相关概念）\n",
    "\n",
    "复试班的计算机视觉部分的讲义\n",
    "https://docs.qq.com/doc/DTFNucmRzc3RlRk5k\n",
    "\n",
    "卷积层是特征提取器，池化层是特征压缩器。他们二者都是在做下采样操作。\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7c6cebda",
   "metadata": {},
   "source": [
    "### 一、数据增强"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "41e8c72f",
   "metadata": {},
   "source": [
    "在图像数据预处理环节，为提升数据多样性，可采用数据增强（数据增广）策略。该策略通常不改变单次训练的样本总数，而是通过对现有图像进行多样化变换，使每次训练输入的样本呈现更丰富的形态差异，从而有效扩展模型训练的样本空间多样性。\n",
    "\n",
    "常见的修改策略包括以下几类\n",
    "1. 几何变换：如旋转、缩放、平移、剪裁、裁剪、翻转\n",
    "2. 像素变换：如修改颜色、亮度、对比度、饱和度、色相、高斯模糊（模拟对焦失败）、增加噪声、马赛克\n",
    "3. 语义增强（暂时不用）：mixup，对图像进行结构性改造、cutout随机遮挡等\n",
    "\n",
    "此外，在数据极少的场景长，常常用生成模型来扩充数据集，如GAN、VAE等。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "ff797e27",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "使用设备: cuda\n",
      "Files already downloaded and verified\n"
     ]
    }
   ],
   "source": [
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.optim as optim\n",
    "from torchvision import datasets, transforms\n",
    "from torch.utils.data import DataLoader\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "# 设置中文字体支持\n",
    "plt.rcParams[\"font.family\"] = [\"SimHei\"]\n",
    "plt.rcParams['axes.unicode_minus'] = False  # 解决负号显示问题\n",
    "\n",
    "# 检查GPU是否可用\n",
    "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
    "print(f\"使用设备: {device}\")\n",
    "\n",
    "# 1. 数据预处理\n",
    "# 训练集：使用多种数据增强方法提高模型泛化能力\n",
    "train_transform = transforms.Compose([\n",
    "    # 随机裁剪图像，从原图中随机截取32x32大小的区域\n",
    "    transforms.RandomCrop(32, padding=4),\n",
    "    # 随机水平翻转图像（概率0.5）\n",
    "    transforms.RandomHorizontalFlip(),\n",
    "    # 随机颜色抖动：亮度、对比度、饱和度和色调随机变化\n",
    "    transforms.ColorJitter(brightness=0.2, contrast=0.2, saturation=0.2, hue=0.1),\n",
    "    # 随机旋转图像（最大角度15度）\n",
    "    transforms.RandomRotation(15),\n",
    "    # 将PIL图像或numpy数组转换为张量\n",
    "    transforms.ToTensor(),\n",
    "    # 标准化处理：每个通道的均值和标准差，使数据分布更合理\n",
    "    transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010))\n",
    "])\n",
    "\n",
    "# 测试集：仅进行必要的标准化，保持数据原始特性，标准化不损失数据信息，可还原\n",
    "test_transform = transforms.Compose([\n",
    "    transforms.ToTensor(),\n",
    "    transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010))\n",
    "])\n",
    "\n",
    "# 2. 加载CIFAR-10数据集\n",
    "train_dataset = datasets.CIFAR10(\n",
    "    root='./data',\n",
    "    train=True,\n",
    "    download=True,\n",
    "    transform=train_transform  # 使用增强后的预处理\n",
    ")\n",
    "\n",
    "test_dataset = datasets.CIFAR10(\n",
    "    root='./data',\n",
    "    train=False,\n",
    "    transform=test_transform  # 测试集不使用增强\n",
    ")\n",
    "\n",
    "# 3. 创建数据加载器\n",
    "batch_size = 64\n",
    "train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True)\n",
    "test_loader = DataLoader(test_dataset, batch_size=batch_size, shuffle=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "301f5f29",
   "metadata": {},
   "source": [
    "注意数据增强一般是不改变每个批次的数据量，是对原始数据修改后替换原始数据。其中该数据集事先知道其均值和标准差，如果不知道，需要提前计算下。"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "583ab9d0",
   "metadata": {},
   "source": [
    "### 二、 CNN模型\n",
    "\n",
    "卷积的本质：通过卷积核在输入通道上的滑动乘积，提取跨通道的空间特征。所以只需要定义几个参数即可\n",
    "1. 卷积核大小：卷积核的大小，如3x3、5x5、7x7等。\n",
    "2. 输入通道数：输入图片的通道数，如1（单通道图片）、3（RGB图片）、4（RGBA图片）等。\n",
    "3. 输出通道数：卷积核的个数，即输出的通道数。如本模型中通过 32→64→128 逐步增加特征复杂度\n",
    "4. 步长（stride）：卷积核的滑动步长，默认为1。\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "15521390",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 4. 定义CNN模型的定义（替代原MLP）\n",
    "class CNN(nn.Module):\n",
    "    def __init__(self):\n",
    "        super(CNN, self).__init__()  # 继承父类初始化\n",
    "        \n",
    "        # ---------------------- 第一个卷积块 ----------------------\n",
    "        # 卷积层1：输入3通道（RGB），输出32个特征图，卷积核3x3，边缘填充1像素\n",
    "        self.conv1 = nn.Conv2d(\n",
    "            in_channels=3,       # 输入通道数（图像的RGB通道）\n",
    "            out_channels=32,     # 输出通道数（生成32个新特征图）\n",
    "            kernel_size=3,       # 卷积核尺寸（3x3像素）\n",
    "            padding=1            # 边缘填充1像素，保持输出尺寸与输入相同\n",
    "        )\n",
    "        # 批量归一化层：对32个输出通道进行归一化，加速训练\n",
    "        self.bn1 = nn.BatchNorm2d(num_features=32)\n",
    "        # ReLU激活函数：引入非线性，公式：max(0, x)\n",
    "        self.relu1 = nn.ReLU()\n",
    "        # 最大池化层：窗口2x2，步长2，特征图尺寸减半（32x32→16x16）\n",
    "        self.pool1 = nn.MaxPool2d(kernel_size=2, stride=2)  # stride默认等于kernel_size\n",
    "        \n",
    "        # ---------------------- 第二个卷积块 ----------------------\n",
    "        # 卷积层2：输入32通道（来自conv1的输出），输出64通道\n",
    "        self.conv2 = nn.Conv2d(\n",
    "            in_channels=32,      # 输入通道数（前一层的输出通道数）\n",
    "            out_channels=64,     # 输出通道数（特征图数量翻倍）\n",
    "            kernel_size=3,       # 卷积核尺寸不变\n",
    "            padding=1            # 保持尺寸：16x16→16x16（卷积后）→8x8（池化后）\n",
    "        )\n",
    "        self.bn2 = nn.BatchNorm2d(num_features=64)\n",
    "        self.relu2 = nn.ReLU()\n",
    "        self.pool2 = nn.MaxPool2d(kernel_size=2)  # 尺寸减半：16x16→8x8\n",
    "        \n",
    "        # ---------------------- 第三个卷积块 ----------------------\n",
    "        # 卷积层3：输入64通道，输出128通道\n",
    "        self.conv3 = nn.Conv2d(\n",
    "            in_channels=64,      # 输入通道数（前一层的输出通道数）\n",
    "            out_channels=128,    # 输出通道数（特征图数量再次翻倍）\n",
    "            kernel_size=3,\n",
    "            padding=1            # 保持尺寸：8x8→8x8（卷积后）→4x4（池化后）\n",
    "        )\n",
    "        self.bn3 = nn.BatchNorm2d(num_features=128)\n",
    "        self.relu3 = nn.ReLU()  # 复用激活函数对象（节省内存）\n",
    "        self.pool3 = nn.MaxPool2d(kernel_size=2)  # 尺寸减半：8x8→4x4\n",
    "        \n",
    "        # ---------------------- 全连接层（分类器） ----------------------\n",
    "        # 计算展平后的特征维度：128通道 × 4x4尺寸 = 128×16=2048维\n",
    "        self.fc1 = nn.Linear(\n",
    "            in_features=128 * 4 * 4,  # 输入维度（卷积层输出的特征数）\n",
    "            out_features=512          # 输出维度（隐藏层神经元数）\n",
    "        )\n",
    "        # Dropout层：训练时随机丢弃50%神经元，防止过拟合\n",
    "        self.dropout = nn.Dropout(p=0.5)\n",
    "        # 输出层：将512维特征映射到10个类别（CIFAR-10的类别数）\n",
    "        self.fc2 = nn.Linear(in_features=512, out_features=10)\n",
    "\n",
    "    def forward(self, x):\n",
    "        # 输入尺寸：[batch_size, 3, 32, 32]（batch_size=批量大小，3=通道数，32x32=图像尺寸）\n",
    "        \n",
    "        # ---------- 卷积块1处理 ----------\n",
    "        x = self.conv1(x)       # 卷积后尺寸：[batch_size, 32, 32, 32]（padding=1保持尺寸）\n",
    "        x = self.bn1(x)         # 批量归一化，不改变尺寸\n",
    "        x = self.relu1(x)       # 激活函数，不改变尺寸\n",
    "        x = self.pool1(x)       # 池化后尺寸：[batch_size, 32, 16, 16]（32→16是因为池化窗口2x2）\n",
    "        \n",
    "        # ---------- 卷积块2处理 ----------\n",
    "        x = self.conv2(x)       # 卷积后尺寸：[batch_size, 64, 16, 16]（padding=1保持尺寸）\n",
    "        x = self.bn2(x)\n",
    "        x = self.relu2(x)\n",
    "        x = self.pool2(x)       # 池化后尺寸：[batch_size, 64, 8, 8]\n",
    "        \n",
    "        # ---------- 卷积块3处理 ----------\n",
    "        x = self.conv3(x)       # 卷积后尺寸：[batch_size, 128, 8, 8]（padding=1保持尺寸）\n",
    "        x = self.bn3(x)\n",
    "        x = self.relu3(x)\n",
    "        x = self.pool3(x)       # 池化后尺寸：[batch_size, 128, 4, 4]\n",
    "        \n",
    "        # ---------- 展平与全连接层 ----------\n",
    "        # 将多维特征图展平为一维向量：[batch_size, 128*4*4] = [batch_size, 2048]\n",
    "        x = x.view(-1, 128 * 4 * 4)  # -1自动计算批量维度，保持批量大小不变\n",
    "        \n",
    "        x = self.fc1(x)           # 全连接层：2048→512，尺寸变为[batch_size, 512]\n",
    "        x = self.relu3(x)         # 激活函数（复用relu3，与卷积块3共用）\n",
    "        x = self.dropout(x)       # Dropout随机丢弃神经元，不改变尺寸\n",
    "        x = self.fc2(x)           # 全连接层：512→10，尺寸变为[batch_size, 10]（未激活，直接输出logits）\n",
    "        \n",
    "        return x  # 输出未经过Softmax的logits，适用于交叉熵损失函数\n",
    "\n",
    "\n",
    "\n",
    "# 初始化模型\n",
    "model = CNN()\n",
    "model = model.to(device)  # 将模型移至GPU（如果可用）\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "292ff0df",
   "metadata": {},
   "source": [
    "上述定义CNN模型中：\n",
    "1. 使用三层卷积+池化结构提取图像特征\n",
    "2. 每层卷积后添加BatchNorm加速训练并提高稳定性\n",
    "3. 使用Dropout减少过拟合\n",
    "\n",
    "可以把全连接层前面的不理解为神经网络的一部分，单纯理解为特征提取器，他们的存在就是帮助模型进行特征提取的。\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "01fb5c0e",
   "metadata": {},
   "source": [
    "#### 2.1 batch归一化"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3d2f2eea",
   "metadata": {},
   "source": [
    "Batch 归一化是深度学习中常用的一种归一化技术，加速模型收敛并提升泛化能力。通常位于卷积层后。\n",
    "\n",
    "卷积操作常见流程如下：\n",
    "1. 输入 → 卷积层 → Batch归一化层（可选） → 池化层 → 激活函数 → 下一层\n",
    "2. Flatten -> Dense (with Dropout，可选) -> Dense (Output)\n",
    "\n",
    "其中，BatchNorm 应在池化前对空间维度的特征完成归一化，以确保归一化统计量基于足够多的样本（空间位置），避免池化导致的统计量偏差\n",
    "\n",
    "旨在解决深度神经网络训练中的内部协变量偏移问题：深层网络中，随着前层参数更新，后层输入分布会发生变化，导致模型需要不断适应新分布，训练难度增加。就好比你在学新知识，知识体系的基础一直在变，你就得不断重新适应，模型训练也是如此，这就导致训练变得困难，这就是内部协变量偏移问题。\n",
    "\n",
    "通过对每个批次的输入数据进行标准化（均值为 0、方差为 1），想象把一堆杂乱无章、分布不同的数据规整到一个标准的样子。\n",
    "1. 使各层输入分布稳定，让数据处于激活函数比较合适的区域，缓解梯度消失 / 爆炸问题;\n",
    "2. 因为数据分布稳定了，所以允许使用更大的学习率，提升训练效率。\n",
    "\n",
    " \n",
    "| **阶段**       | **均值/方差来源**          | **参数更新**               |  \n",
    "|----------------|---------------------------|---------------------------|  \n",
    "| **训练阶段**   | 基于当前批次数据计算       | 实时更新 $gamma$、$beta$ |  \n",
    "| **推理阶段**   | 使用训练集的**全局统计量**（如滑动平均后的均值和方差） | 不更新参数，直接使用固定值 | \n",
    "\n",
    "\n",
    "深度学习的归一化有2类：\n",
    "1. Batch Normalization：一般用于图像数据，因为图像数据通常是批量处理，有相对固定的 Batch Size ，能利用 Batch 内数据计算稳定的统计量（均值、方差 ）来做归一化。\n",
    "2. Layer Normalization：一般用于文本数据，本数据的序列长度往往不同，像不同句子长短不一，很难像图像那样固定 Batch Size 。如果用 Batch 归一化，不同批次的统计量波动大，效果不好。层归一化是对单个样本的所有隐藏单元进行归一化，不依赖批次。\n",
    "\n",
    "ps：这个操作在结构化数据中其实是叫做标准化，但是在深度学习领域，习惯把这类对网络中间层数据进行调整分布的操作都叫做归一化 。"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "558cfb90",
   "metadata": {},
   "source": [
    "#### 2.2 特征图"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0ddc9921",
   "metadata": {},
   "source": [
    "卷积层输出的叫做特征图，通过输入尺寸和卷积核的尺寸、步长可以计算出输出尺寸。可以通过可视化中间层的特征图，理解 CNN 如何从底层特征（如边缘）逐步提取高层语义特征（如物体部件、整体结构）。MLP是不输出特征图的，因为他输出的一维向量，无法保留空间维度\n",
    "\n",
    "特征图就代表着在之前特征提取器上提取到的特征，可以通过 Grad-CAM方法来查看模型在识别图像时，特征图所对应的权重是多少。-----深度学习可解释性\n",
    "\n",
    "我们在后续介绍。下面接着训练CNN模型"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a5a8b05d",
   "metadata": {},
   "source": [
    "#### 2.3 调度器"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "f6daa475",
   "metadata": {},
   "outputs": [],
   "source": [
    "criterion = nn.CrossEntropyLoss()  # 交叉熵损失函数\n",
    "optimizer = optim.Adam(model.parameters(), lr=0.001)  # Adam优化器\n",
    "\n",
    "# 引入学习率调度器，在训练过程中动态调整学习率--训练初期使用较大的 LR 快速降低损失，训练后期使用较小的 LR 更精细地逼近全局最优解。\n",
    "# 在每个 epoch 结束后，需要手动调用调度器来更新学习率，可以在训练过程中调用 scheduler.step()\n",
    "scheduler = optim.lr_scheduler.ReduceLROnPlateau(\n",
    "    optimizer,        # 指定要控制的优化器（这里是Adam）\n",
    "    mode='min',       # 监测的指标是\"最小化\"（如损失函数）\n",
    "    patience=3,       # 如果连续3个epoch指标没有改善，才降低LR\n",
    "    factor=0.5        # 降低LR的比例（新LR = 旧LR × 0.5）\n",
    ")"
   ]
  },
  {
   "attachments": {
    "优化器与调度器对比.png": {
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"
    }
   },
   "cell_type": "markdown",
   "id": "b68588f5",
   "metadata": {},
   "source": [
    "ReduceLROnPlateau调度器适用于当监测的指标（如验证损失）停滞时降低学习率。是大多数任务的首选调度器，尤其适合验证集波动较大的情况\n",
    "\n",
    "这种学习率调度器的方法相较于之前只有单纯的优化器，是一种超参数的优化方法，它通过调整学习率来优化模型。\n",
    "\n",
    "常见的优化器有 adam、SGD、RMSprop 等，而除此之外学习率调度器有 lr_scheduler.StepLR、lr_scheduler.ExponentialLR、lr_scheduler.CosineAnnealingLR 等。\n",
    "![优化器与调度器对比.png](attachment:优化器与调度器对比.png)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "12867b81",
   "metadata": {},
   "outputs": [],
   "source": [
    "# scheduler = optim.lr_scheduler.StepLR(optimizer, step_size=5, gamma=0.1)  \n",
    "# # 每5个epoch，LR = LR × 0.1  \n",
    "\n",
    "# scheduler = optim.lr_scheduler.MultiStepLR(optimizer, milestones=[10, 20, 30], gamma=0.5)  \n",
    "# # 当epoch=10、20、30时，LR = LR × 0.5  \n",
    "\n",
    "# scheduler = optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=10, eta_min=0.0001)  \n",
    "# # LR在[0.0001, LR_initial]之间按余弦曲线变化，周期为2×T_max  "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "54174e89",
   "metadata": {},
   "source": [
    "可以把优化器和调度器理解为调参手段，学习率是参数\n",
    "\n",
    "注意，优化器如adam虽然也在调整学习率，但是他的调整是相对值，计算步长后根据基础学习率来调整。但是调度器是直接调整基础学习率。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "4ff5191d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "开始使用CNN训练模型...\n",
      "Epoch: 1/20 | Batch: 100/782 | 单Batch损失: 1.6939 | 累计平均损失: 2.0402\n",
      "Epoch: 1/20 | Batch: 200/782 | 单Batch损失: 1.7632 | 累计平均损失: 1.9162\n",
      "Epoch: 1/20 | Batch: 300/782 | 单Batch损失: 1.6051 | 累计平均损失: 1.8418\n",
      "Epoch: 1/20 | Batch: 400/782 | 单Batch损失: 1.6624 | 累计平均损失: 1.7934\n",
      "Epoch: 1/20 | Batch: 500/782 | 单Batch损失: 1.7259 | 累计平均损失: 1.7492\n",
      "Epoch: 1/20 | Batch: 600/782 | 单Batch损失: 1.3839 | 累计平均损失: 1.7149\n",
      "Epoch: 1/20 | Batch: 700/782 | 单Batch损失: 1.7046 | 累计平均损失: 1.6879\n",
      "Epoch 1/20 完成 | 训练准确率: 38.46% | 测试准确率: 54.70%\n",
      "Epoch: 2/20 | Batch: 100/782 | 单Batch损失: 1.5352 | 累计平均损失: 1.4148\n",
      "Epoch: 2/20 | Batch: 200/782 | 单Batch损失: 1.3239 | 累计平均损失: 1.3802\n",
      "Epoch: 2/20 | Batch: 300/782 | 单Batch损失: 1.4556 | 累计平均损失: 1.3511\n",
      "Epoch: 2/20 | Batch: 400/782 | 单Batch损失: 1.2608 | 累计平均损失: 1.3269\n",
      "Epoch: 2/20 | Batch: 500/782 | 单Batch损失: 0.9699 | 累计平均损失: 1.3041\n",
      "Epoch: 2/20 | Batch: 600/782 | 单Batch损失: 1.1444 | 累计平均损失: 1.2882\n",
      "Epoch: 2/20 | Batch: 700/782 | 单Batch损失: 1.0800 | 累计平均损失: 1.2723\n",
      "Epoch 2/20 完成 | 训练准确率: 54.43% | 测试准确率: 65.78%\n",
      "Epoch: 3/20 | Batch: 100/782 | 单Batch损失: 1.4134 | 累计平均损失: 1.1310\n",
      "Epoch: 3/20 | Batch: 200/782 | 单Batch损失: 1.1853 | 累计平均损失: 1.1286\n",
      "Epoch: 3/20 | Batch: 300/782 | 单Batch损失: 1.3804 | 累计平均损失: 1.1145\n",
      "Epoch: 3/20 | Batch: 400/782 | 单Batch损失: 0.9816 | 累计平均损失: 1.1048\n",
      "Epoch: 3/20 | Batch: 500/782 | 单Batch损失: 1.0868 | 累计平均损失: 1.1030\n",
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      "Epoch 3/20 完成 | 训练准确率: 61.76% | 测试准确率: 69.60%\n",
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      "Epoch 4/20 完成 | 训练准确率: 65.27% | 测试准确率: 70.09%\n",
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      "Epoch 5/20 完成 | 训练准确率: 67.36% | 测试准确率: 73.07%\n",
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      "Epoch 6/20 完成 | 训练准确率: 69.02% | 测试准确率: 74.49%\n",
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      "Epoch 7/20 完成 | 训练准确率: 70.61% | 测试准确率: 75.70%\n",
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      "Epoch 8/20 完成 | 训练准确率: 71.69% | 测试准确率: 75.77%\n",
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      "Epoch 9/20 完成 | 训练准确率: 72.65% | 测试准确率: 76.66%\n",
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      "Epoch 10/20 完成 | 训练准确率: 73.45% | 测试准确率: 77.76%\n",
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      "Epoch 11/20 完成 | 训练准确率: 73.87% | 测试准确率: 78.38%\n",
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      "Epoch 12/20 完成 | 训练准确率: 74.55% | 测试准确率: 76.79%\n",
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      "Epoch: 13/20 | Batch: 700/782 | 单Batch损失: 0.6725 | 累计平均损失: 0.7171\n",
      "Epoch 13/20 完成 | 训练准确率: 74.96% | 测试准确率: 78.93%\n",
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      "Epoch: 14/20 | Batch: 500/782 | 单Batch损失: 0.7194 | 累计平均损失: 0.6999\n",
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      "Epoch 14/20 完成 | 训练准确率: 75.38% | 测试准确率: 78.84%\n",
      "Epoch: 15/20 | Batch: 100/782 | 单Batch损失: 0.7210 | 累计平均损失: 0.6652\n",
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      "Epoch: 15/20 | Batch: 700/782 | 单Batch损失: 0.9048 | 累计平均损失: 0.6794\n",
      "Epoch 15/20 完成 | 训练准确率: 76.22% | 测试准确率: 79.55%\n",
      "Epoch: 16/20 | Batch: 100/782 | 单Batch损失: 0.7686 | 累计平均损失: 0.6652\n",
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      "Epoch: 16/20 | Batch: 400/782 | 单Batch损失: 0.6138 | 累计平均损失: 0.6667\n",
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      "Epoch 16/20 完成 | 训练准确率: 76.64% | 测试准确率: 79.35%\n",
      "Epoch: 17/20 | Batch: 100/782 | 单Batch损失: 0.6456 | 累计平均损失: 0.6715\n",
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      "Epoch: 17/20 | Batch: 700/782 | 单Batch损失: 0.7612 | 累计平均损失: 0.6666\n",
      "Epoch 17/20 完成 | 训练准确率: 76.61% | 测试准确率: 79.48%\n",
      "Epoch: 18/20 | Batch: 100/782 | 单Batch损失: 0.7169 | 累计平均损失: 0.6318\n",
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      "Epoch: 18/20 | Batch: 400/782 | 单Batch损失: 0.6704 | 累计平均损失: 0.6524\n",
      "Epoch: 18/20 | Batch: 500/782 | 单Batch损失: 0.5147 | 累计平均损失: 0.6497\n",
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      "Epoch: 18/20 | Batch: 700/782 | 单Batch损失: 0.7000 | 累计平均损失: 0.6522\n",
      "Epoch 18/20 完成 | 训练准确率: 77.18% | 测试准确率: 80.24%\n",
      "Epoch: 19/20 | Batch: 100/782 | 单Batch损失: 0.6252 | 累计平均损失: 0.6400\n",
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      "Epoch: 19/20 | Batch: 400/782 | 单Batch损失: 0.5730 | 累计平均损失: 0.6506\n",
      "Epoch: 19/20 | Batch: 500/782 | 单Batch损失: 0.4767 | 累计平均损失: 0.6506\n",
      "Epoch: 19/20 | Batch: 600/782 | 单Batch损失: 0.7195 | 累计平均损失: 0.6502\n",
      "Epoch: 19/20 | Batch: 700/782 | 单Batch损失: 0.6597 | 累计平均损失: 0.6491\n",
      "Epoch 19/20 完成 | 训练准确率: 77.50% | 测试准确率: 81.04%\n",
      "Epoch: 20/20 | Batch: 100/782 | 单Batch损失: 0.6868 | 累计平均损失: 0.6206\n",
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      "Epoch: 20/20 | Batch: 400/782 | 单Batch损失: 0.3314 | 累计平均损失: 0.6360\n",
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      "Epoch: 20/20 | Batch: 700/782 | 单Batch损失: 0.5281 | 累计平均损失: 0.6383\n",
      "Epoch 20/20 完成 | 训练准确率: 77.61% | 测试准确率: 80.98%\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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t7c39+/f54YcfqF+/Pvfv308y9A/USeGSJUtYtmwZM2fOJDIykgoVKlCvXj1GjBiBXq9nxowZ5MuXj2PHjrFixQomTZpEjRo1nuu7EUIIIUTakXOu1J9z5c+fnzt37vD999+bJ20pX758su3Cw8OfWDMzb968XL58mRs3bnDnzh3zY/PmzYCqh7VmzZok7924cYPLly8/V7uEEC9GakoJYeMuX75MiRIlOH78uLmu0ZPcuXOHpk2bkiNHDiZNmsTZs2dxcXFh7969jBgxAicnJxwcHPj222/Zu3cvW7duRa/XJ5tqd9SoURw/fpxDhw5RtmxZXFxcWL16NR9++GGyxMuTLFiwgOzZs1OxYkW+/vprDh48SP78+XFycmLixIlUqlSJ48ePp6pYZXR0dIrd6KtXr8706dOf+Lkff/yRDh064OHhYV53+PBhihcvDqieTHFxcRQrVgyj0YibmxtBQUEAFCtWjD59+jB16tRk+42MjOTOnTv4+vpy69Ytbty4ga+vb7KiqK6urvz777/mXlr9+/enf//+AMyePZt69erx1ltv0b59e/PdxMGDB5OQkEBiYqL5jqUQQggh0p+ccz3/OZe9vT1Hjx5Fp9MxefLkJO89eryQkBBatmyZ5H1T0s3e3j5ZElDTNGbPnk2bNm0oVaoUY8aM4ciRIzIZjBBWID2lhLBhv/zyC02aNGH37t0EBwfTrFkz4MndsHPnzo2npycVKlSgVq1azJ49m61bt+Lm5saZM2eoXr16ku7gK1asoFGjRknuxI0bN46pU6fy008/JSkqWbRoUX777TfzOi8vL3MX6senO46Pj2f58uUMGzaMAgUKMHr0aHbs2EHTpk1ZsWIF7du3p0SJEsTHxz/zBOn+/fs0bdqUH374Icn62NhYGjZsyPXr1zl//jyJiYmEhYWZ3z948CAHDhygT58+ST5n6tV0//59pk6dStGiRYmIiCAqKsqckHpcs2bN0Ol0XL16FVAnouXLl2fr1q3mwudbt25l06ZNST7Xvn173NzcsLe3T9btPD4+nk6dOqHT6bCzszOvt7Ozw8XFhV27dj31exFCCCFE2pFzrhc/50qpKHxiYiI1a9YkR44chISEcO7cOXNPcHd3d1q0aPHUWL744guOHDnC9OnTmTBhAq6urvj4+HDp0qWnfk4IkfYkKSWEDYqNjQVg5syZTJkyhfnz59OuXTty584NYJ6hxcRUONLZ2Zm///6bNWvW8MEHHxAVFcWMGTN48OABXbp0oXTp0kmmF27SpAnnzp1j/Pjx5nV6vZ4RI0bQvn37FGMz9QY6e/asuWB4vXr1kvQSmjdvHnZ2duaEkL+/Pxs3bqR+/fp89NFH9OvXD4CpU6eyY8eOJ34PBoOBAQMGkJiYiK+vLxcvXiQyMpJ69erRv39/KlasSNmyZVmyZAmBgYHUq1eP69evA6pIealSpWjYsGGqvnNN09Dr9cTExCR7z1Sks0SJEuj1eo4dO0bVqlWfuc+NGzcSExODwWBIVuTT2dmZ9evXJ1tvMBiIjY2ladOmqYpbCCGEEC9OzrmUlznnSomDgwOHDx+mevXqDBs2jOLFi1O4cGEaN27MzZs32bFjR7JanAAPHjygV69eTJ8+nY0bN1KyZElzD7ScOXNSqVIlPv74Y27fvv3EYwsh0paM3RDCBh06dAiADz/8EEdHR3755ReOHDlifr9QoUJMmjTJvJyQkEBCQgIXLlxg48aN7N27lwMHDuDl5UXLli1p164dxYoVY/ny5djZ2ZGQkIBOp6NgwYLMmjWLPn360L59e6pWrcqECRPMdwUNBkOSZ3hYl8DDw4OcOXMC6sTDdPctODiYCRMm0L9/f/OwtVGjRlGyZEn69etHkSJFaN26Na1bt6ZLly7J9m9y6dIlbt++Tf369Vm+fDmvvfYaUVFRtGzZkubNm/PGG28Aarjb8OHDcXFx4eLFizg7OxMREUFgYCADBw5McvdO0zT27duX7I7eo8tlypThwoUL5uVHYzMd8+eff2b9+vXJYn78bqqrq2uybZ7F1FNKCCGEEOlPzrle7pwLICYmhpiYGMLCwpIMT/T392fYsGHs3LmT3bt3Y2dnR+HChfHx8aFjx458++23eHl5ARAVFcWaNWsYN24c8fHx/PbbbzRp0sS8r5w5c7Jr1y4mTpzIlClTmDdvHj4+Pvzwww+pmqVQCPESNCGEzTlz5ozWsGFDLTY2Vlu6dKk2dOjQp25vZ2enHTx4UFuxYoXm4eGhDRw4UDt16pSmaZo2evRorUqVKtqDBw+0CxcuaH369NG8vLy0Dz74QNM0TTMajVrdunW1//3vf8n2e/PmTQ3QLly4YF4XFxenHThwQIuPjzevCwsL0yIiIjRN07QdO3Zobm5uWkBAgKZpmjZz5kwN0H777Tfz9j169NAqV66saZqmrVy5UmvcuLEGaGFhYeZtIiMjtT59+mjR0dGapmnavn37tHv37iWLUa/XazVr1tQArXr16knijI2NTbLt4MGDtTp16mh37txJ9rh9+7Z28+ZN7fr160k+U7p0aW3Pnj2apmlaQkKC5uvrq9WqVcv8/vLlyzUfHx/t7t272qBBgzTA3PancXBw0NauXfvM7YQQQgiRfuSc6+XPuUaNGqUBWo4cObTLly9rR44c0Ro0aKA5ODho5cuX144dO5ZkP3///bf2yiuvaEWKFNHi4uK0mJgYrUyZMppOp9O6deumBQUFPfVncOnSJa1Xr15ajRo1tMTExKduK4R4eTpNe6xyrhBCPEFsbCwGgwE3NzfzupiYGCIjI8mfPz8Gg4H27dtTrlw5PvnkE/Lnzw+Q5kW1g4KCzHUQfv75Z86dO8eXX35pfj8kJAS9Xk+hQoXYtWsXs2bN4t1336Vz584vdLzw8HBmz57N22+/TZkyZZ643Ycffsj58+fZu3fvCx1H0zR27NiBl5cXFSpUACAiIoL79+9TuHBhWrVqha+vL4MGDXrqfgwGAw4ODixdupR33333hWIRQgghhPXIOdfDc66bN28SEBBA9erVyZYtGwaDgUGDBtG4cWPeeust7OySV6SJjIzk9OnT1K1bF4AjR47g4uKSZMbkZzEajSnuWwiRtiQpJYQQQgghhBBCCCEsTlK/QgghhBBCCCGEEMLiJCklhBBCCCGEEEIIISxOklJCCCGEEEIIIYQQwuIkKSWEEEIIIYQQQgghLC7tpmbIAIxGI7dv38bd3R2dTmftcIQQQgiRCWmaRmRkJIUKFbLJmZfkfEoIIYQQLyu151NZKil1+/ZtvLy8rB2GEEIIIbKAmzdvUqRIEWuHYXFyPiWEEEKItPKs86kslZRyd3cHVKM9PDysHI1l6fV6/vjjD5o3b46jo6O1w7EoW2472Hb7bbntIO235fbbctsh/dsfERGBl5eX+bzC1tjq+ZT8XUn7bbX9ttx2kPbbcvttue2Qcc6nslRSytTF3MPDw6ZOokD9Qrm6uuLh4WFzf1C23Haw7fbbcttB2m/L7bfltoPl2m+rQ9ds9XxK/q6k/bbafltuO0j7bbn9ttx2yDjnU7ZXKEEIIYQQQgghhBBCWJ0kpYQQQgghhBBCCCGExUlSSgghhBBCCCGEEEJYXJaqKZVaBoMBvV5v7TDSlF6vx8HBgbi4OAwGg7XDsShbrfkhhBBCCCGEEFmd0WgkISEhzfdry9fQ8PLtd3R0xN7e/qXjsKmklKZpBAUFcf/+fWuHkuY0TaNAgQLcvHnT5pI0Op0OOzvp9CeEEEIIIYQQWUlCQgL+/v4YjcY037ctX0ND2rQ/Z86cFChQ4KW+P5tKSpkSUvny5cPV1TVL/eIZjUaioqJwc3OzqQSN0Wjk1q1b5MyZE03TrB2OEEIIIYQQQog0oGkad+7cwd7eHi8vrzS/zrXVa2iTl2m/pmnExMQQHBwMQMGCBV84DptJShkMBnNCKk+ePNYOJ82ZujS6uLjY3B9U3rx5efDggU12uRRCCCGEEEKIrCgxMZGYmBgKFSqEq6trmu/flq+h4eXbny1bNgCCg4PJly/fCw/ls5lv3lRDKj1+mYV1OTo6otPpJCklhBBCCCGEEFmE6frOycnJypGIJzHlV16mZrfNJKVMstKQPaGYfqYyfE8IIYQQQgghsha5hs+40uJnY3NJqcwqMTExzfb1PMmbe/fuERISAsDp06efO461a9dy6tSpVG8fFxfHpUuXnusYQgghhBBCCCFERmKta/jMxmZqSmVmf//9N5988gm//fYbefPmBSAmJibZUMTY2FjCw8Px9vYGYNeuXSQmJtKyZcsk2/n4+PDNN99Qp06dJOsTExM5c+YMVapUMa9bsWIF3333HRcvXuSzzz4jd+7crFy5Mtlxg4KCcHR0BFQ3S09PT0JDQ+nbty+7d+9OdVv37NnDO++8Q1BQkHTTFEKIzOLuXfj3X/U4fRoSE8HBId0eOqDAyZPQqBH893+PyBzu3YMRI9Svyd9/gw2W8BBCCGEDnucaPjg4mGLFigHPfw1vUqdOHT7//HPatWtHfHw8Li4ulC9f3vx+dHQ0ZcuWZdu2bU+MWdM0ChUqxJ9//knZsmVfqN0vQpJSmUC9evVo3rw5DRs2ZN++fXh6etKlSxe6du1Kly5dWLRoEX369OH8+fN8/fXX7NmzB4BvvvmGN998k5YtWxIfH4+TkxMBAQGcP3+e1157DVCJKKPRiJOTE//88w9vvPEGW7ZsoUGDBgCsX7+eDz74AAcHB9atW8fo0aOJi4vD2dkZo9GIvb09Z86coV+/fty6dQsnJyfy5s3LjBkzmDhxIvny5WPgwIGEhYUBcObMGbJnzw7AjRs3qFq1Kp6enri5uQHqj9LDw4PatWubi61FRkYSGhrKjRs3zJ8VQghhJQ8ewNGjD5NQ//4LN29aNAQHoBag79cPPDwsemzxctzdYeVKiImBc+egQgVrRySEEEKkvdRcw7/33nucOXOGzz777IWv4Y1GI3Z2djg7O+Pi4oLBYMDZ2RmdToefnx8ODirls3fvXr7//vtkcR44cIA5c+awfft2dDodoaGh5MiRw3JfFJKUyjQmTJhAfHw8ly9fxtPTk2zZsuHi4oJOp+ODDz6gT58+ODo6mivenz17lvDwcAYNGkRwcDDVqlVDp9Nx//59HBwcePXVVwHVq6ldu3bMnj2bunXrMnPmTFq3bs2ePXuwt7fnr7/+4tq1a8yZM4eoqCg0TWPjxo1ER0fzySefMGbMGGrUqIGfnx/NmjVj2LBhtGzZkgEDBmA0Gjl37hwuLi40adKEIUOGJEkqFS1alJMnT1KoUCF0Oh1xcXFUrFiR+fPn07RpU/MfkL+/Py4uLpKQEkIIS4uJAT+/pAmolIZY63RQtizUqAGvvQbZs6veUs96GAyp2+6xh1Gv535oKO4uLhb/SsTLcXSEunVh1y7Yt0+SUkIIIbKuZ13Dv/feey99Df/RRx+xbNky4uLiaNeuHfXq1WP79u1omsY777xjrvkUHBxsni3vUevWraNWrVrm7ezs7JJsFx8fj52dnXlUVHqQpFQGFxkZSXh4OE5OTnz55Ze4u7sDmLOfgDl586gJEyYwa9YsYmJiKFeuHHv27KFChQpUrFiRNWvWUOEJZ4F9+vQhMDCQPHnyMHz4cAD27dtHyZIlGTduHKGhofzvf/9L9rm4uDguXrxIkyZNMBqNVK5cmQcPHlC/fn30ej1nzpzhwYMHjB8/nj59+jBgwAAAunXrRpEiRViyZAnTpk2jVq1aNGnShMaNG/P+++/TrFkz3nzzTVq3bs0333yTJt+pEEKIFOj1akzVowmos2dV4uhx3t4qAWV6VK2qusBYiEGv58C2bbTKk8dixxRpx8dHJaX274eBA60djRBCCJG2LHkNP3fuXObOnUvDhg0ZMWIELVu2xGg0cuLEiWTbuj92rnb58mV+/vlnsmfPzpw5cwBISEigZMmS5jjj4uKYOXMm/fr1e7kv5SmslpRauHAh48ePJywsjJo1a/LTTz/xyiuvcObMGXr37s2VK1d47733mDp1arpV29c0dRPY0lxd1U3l1Ni3bx/Dhg0jPDycbt26MWvWLODpRdM2bNjAiRMnOHnyJDNmzODNN9/kgw8+4M6dOwQHB9O9e3ciIyO5evVqip//8ssv2blzJ9u3b8fZ2TnZ+0ajkYSEhCR/VGvXrkXTNAYNGgTAt99+y6+//sqSJUsIDAxkxYoVrFixgnHjxhEcHGze12+//cann37KsmXLmDBhAv3796dr164ULlyYjh07MmbMGLp3786oUaNS94UJIYR4NqMRLl5MmoDy84P4+OTbFiiQNAFVvTp4elo8ZJF1/FchgH371LmYTKokhBAiNdL6+t1ohOhosLd/do3DjH4NbxIZGcnkyZOZO3fuE2OrXLkyer2ePn36MGTIECZOnIidnR2JiYk4Ojpy+/btFPMA6cUqSamrV6/y1VdfsWnTJjw9PRk/fjy9evVi586dtG7dmhYtWrBmzRo+/vhjlixZQu/evdMljpgY+K+UkUVFRamRDanh6+uLr68v48aNIzo62rw+NjY2xewqQI4cOXj99dcJCAhg9+7dXLx4kQIFCtC3b18aNmxIp06dyJ8//1OPGxsby7hx45gwYQINGzbEwcGB+/fvYzQa2bx5M4mJiRw5cgQvLy9iY2MZP348FSpUoHbt2gwdOpRp06Y9cd+Pxu3m5sagQYNwcHBg7dq1rFixggIFCtCxY0eOHTtGjx49uHHjRuq+LCGEEMlpGly9CsePP6wFdewYREYm3zZnTpV0ejQJVbiwZA1EmqpZE5ydVX38S5egTBlrRySEECIzSPvrdzsgZ6q2zMjX8IGBgYSFhTF48GAaNmxIzpw5GTJkCOPGjePOnTvkyZMHJycnihcvbp5MLDo6mm7duvH222+b93Pv3j0cHR2TJaQM//WaNw0zTGtWmfPkxIkT1K5dm6pVq1K0aFH69OnDlStX2L59Ow8ePGDGjBmUKFGCSZMmsWjRImuEmCE9+ksQHBycrPudSdOmTfnxxx+5e/cuX3/9NQUKFODChQv8/vvvdOrUCcDcw0mv1yfL2L733nuULFnSPHxv7969BAQEMGTIEN59911u3LjB7du38fLyAmDAgAHcu3eP8uXL06tXL1xdXXF0dESv19OlSxc+/vhjtm/fTvXq1VmwYIH5OHfv3mXYsGG0bduWEydOMHv2bMLDwylXrhzff/89bdu2JSgoiDFjxtCpUyfi4uLS7ssUQoisyGCACxdg1SoYNkzNTpcrF5QqBW+/DdOmwd69KiGVLRvUqwdDhqjK05cuqanRdu6ESZOgfXsoUkQSUiLNubhA7drq9f791o1FCCGESE/pfQ3/0UcfUapUKQICAhg4cCA//PBDkpFmb7/9NgsXLjQvm97bsGEDQ4cOpVSpUuTLlw9PT09KliyJXq/Hw8MDT09PPD09yZMnD9mzZ2fFihVp+8U8wio9pcqVK8eff/6Jn58f3t7ezJ07l2bNmnHy5Elq165tniaxUqVKnDt37on7iY+PJ/6RoQYRERGA+iHp9fok2+r1ejRNw2g0YjQaAXVS9N9HLMrFRXUVfB6appnjBzh//jze3t7mZU3TzM+m3kzXr1/np59+Iioqis2bNxMVFcWrr76KpmlERERQvHhx4uLiGD16tLnGk7+/P5s3b2b27Nnmfbdp0wYnJyeCgoJITExkz549REZGcuDAAby8vPjggw8oVaoUYWFh5s8YjUZiYmJYtWoVgYGBrFy5kuXLlzN+/HhzjCEhIZw6dYrDhw9z5coVypQpQ968eRk/fjy7d+9m3rx5JCYm8tdff7F161bz7AIpfTegukM+/nO3BaY2S9ttj7Tfdtuv1+vRGQwk+vmhO3UKnZ8fuhMn1PMjd+RMNGdntIoV0V57Da16dbRq1aBcOXj8bt1TupVnJOn9s7fF3ylLa9BADd/btw/SsUyFEEKILMTVVfVYSitGo5GIiAg8PDzMM78/7dgv6/z585QoUeKJ75uu4ZcsWUJMTAxbtmx54jX8mDFjGDhwIL1792bs2LF06tSJ0qVLm9tlEhUVRZUqVczLpl5Pffv2pXfv3kna37NnT3bt2kWpUqXYs2dPupVRepzVklIdO3Y0T2no7e3NkSNHmDJlCt7e3ubtdDod9vb2hIeHkytXrmT7mTx5MuPHj0+2/o8//jAntkwcHBwoUKAAUVFRJCQkpHGLnk9KIyaexZSAW758OaVKlSJbtmx4eHiYE3GR/+3UYDBw7tw5evToQYkSJShdujQ6nY7Dhw/To0cPYmJiyJ49O2XKlOH48ePcvn2bIkWKmPczZcoUOnfubD6epmmsWLGCV155hSlTphAWFpZkaF5ERARlypTh2LFj3Llzh4iICPMfTHBwML169SI+Pp6QkBAaNGjAjRs3ePfdd4mIiCB37tysXLkSe3t7ypQpw6RJkzh9+jQLFy6kSJEiTJw4EVAXB40aNSI4OBiXFGZaMv08Dx48+NRxulndzp07rR2C1dhy20Habwvt1+n1eNy8SY6rV8l57Ro5rl7lzYAA7FP4/yzRyYkIb2/ulyjBgxIluP/KK0R6eaE9moAKDFSPTC69fvYx1ig4aWN8fODrr6WulBBCiNTT6VI/hC41jEbVyTx79mfXlHoZP//8M6+++irZs2enSJEiKW5z8+ZN3nnnHUqXLm2+hj937hwfffQR0dHRuLm5UaBAAa5du8atW7fMo5aqVauWbF/29va4uLgQExPD1atXqVixIgBFihRJsZMHwJ49e9iyZQsnT56kffv2DB8+nGnTpj0zWZcWrJKU+ueff9iyZQuHDx/m1VdfZerUqbRq1YrGjRsnG79o+jJTSkqNHDmSoUOHmpcjIiLw8vKiefPmeHh4JNk2Li6Omzdv4ubmlmJiI6NzcnJiw4YN/Pvvv9SuXZu33nrL3MbExERzN0B7e3vKlSvHlClTKFeunPmX1MXFhcGDBxMaGsrSpUvR6XQcOXKEQYMGcezYMdzd3Tl//jzLli3j1KlT5n3rdDrc3Nzw8PDA2dkZJycnPDw8MBqNJCYmmsekuri4sHr1avbs2UNwcDARERH4+/tz+/Ztjh07lqSnlIODAx4eHnTp0oUbN26Y9wFqnG1ISAgNGzZM0n6DwUC9evVSLNgWGxsLQN26dXGzRpEwK9Pr9ezcuZNmzZql61SdGZEttx2k/Vm2/XFx6M6cUT2fjh+HEyfUcgoJKM3dHa1KFdUDqkoVtKpVoUwZ3O3tcQe8LB+9RaT3zz7CGt2obUydOqqjXmAgBASoCR2FEEKIrETTNDZs2MCBAweoU6cO7du3N7/3eGcKLy8vpk6dmuQaXqfTMWzYMEJDQ1m2bBmgbsgNHDiQEydOPHEo4OTJkwEYMWIEjRs3Zu3atQQFBbFr164UcyE7duzgnXfeYcWKFXh5ebF582ZatmxJs2bNmDp1aoqJr7RklaTU6tWr6dKlC7Vq1QLU1Ifz5s2jY8eOnDlzJsm2kZGRSZIWj3J2dk6xKryjo2Oyk1SDwYBOp8POzs4i2b60FhAQQEREBD/++CPt2rXj33//xc7ODoPBQMOGDdHpdOj1eoxGIzqdDh8fH/bt28e0adPw8/Nj6NCh/Pnnnxw4cMDcDe+NN96gZs2aDB06lEWLFnHy5El8fX0p80jFUYPBkGQZMCeGChcuTOB/d9o1TaNXr158++239OjRg3Xr1tG4cWPy58+PnZ2d+bsHzK937NiRrJ1+fn74+vri5+eX6u/m0Wk1s9SF6XNK6ffeVthy28FG2m80gr8/nDxpfjicPUvTyEiyFSiAzsMD3N3V49HXqVl2cUnbbhqaBgkJEBsLcXFJn5+0LioKzpxRxcjPnk15GF3OnFC1KlSrRmKlSuyNiMCnb18cLTg7SkaTXr/7Wf7vKQNwdVV19A8dUr2lJCklhBAiq/H39092DQ+Yr+FB3WgzGAxomsbrr7/Ovn37+Oabb8zX8Lt37+bAgQPmkjUtWrSgZs2aDBkyhEWLFhEaGsqRI0e4du2aOeF06tQpJk6cyIkTJ9i3bx/h4eGMGjWKsmXLMm/ePFq2bAnAuXPnmDx5Mjt27GD58uW8+eabABQqVIi//vqL4cOHU7NmTerUqcNnn31GmzZt0uV7skpSymg0Ehoaal6OjIwkJiYGBwcHDh06ZF7v7+9PfHw8uXPntkaYGYpOp2Pp0qXcuHHDXMwMVM+oXbt2YTQaiY+PJyEhgWXLlvHRRx/xzjvvMGrUKBITE+nVqxf79u3j/PnzbNmyxfydzpw5kxo1ahAcHEy3bt3o0qVLkuPGxcVx+fJlSpYsmWS90WhMUnOjQIEC5uTQrFmzaNq0KatXrwZU1rdRo0YcOnSINWvW8NVXXz2xnY/XCRNC2KDoaDh9OkkCitOnk4191gHZQU3h9TIcHJ6etHJ2VgmkZyWWHl3334nDC/P0hGrVVBLqv0QUxYubk2eaXk/0tm3p29dciHTm4/MwKdWrl7WjEUIIIdLWs67hgZe+hnd1dWX06NH4+vpSq1YtvvrqK6ZNm0a/fv344YcfyJkzJwULFuTXX39lxYoVzJs3jxYtWnDmzBlq1KhB69atOXbsGMWLF08Su4eHBwsWLKB///5MmTIl2ftpySpJqddff52ePXtStWpV8ufPz8KFCylQoAAff/wx33zzDYsXL6Z3795MmjSJpk2bptvUg5nJ0qVLn7lNnTp1OHDgALGxsbz11lvm7nxGo5H9+/dTtGhRbt++TXx8PKtWrQIgf/78XLlyxdwb7fFeZE8q9mpnZ5ekl5opqwqQJ08eTpw4YV4uX7485cuXJyYmhkOHDj01yVirVi1CQkKe2VYhRBagaXDzZtLk08mTcOVKykkdJyeoUAEqV4bKlUksV46Dx49Tt0IFHGJj1cwVkZEPH48up/SeqSB4YiKEh6tHWtPp1Cx3Li7q+dHXj67Llk3NkGdKRMmsd8IG+PjAlCkyA58QQoisKTXX8PXr1+fvv/8mOjr6ha/hH732HjFiBJ988kmKQ/u6d+9O9+7dAahYsaK5lM7jpY8eVa1aNdavX5/6Rr8AqySlOnTowPnz55k1axZ37tyhQoUK/PLLLzg6OrJw4UK6du3Kp59+ip2dHXv37rVGiJla9seqv9nZ2VG0aFFAJa7q1KmT5P0nDY9Ma66urskK0AshbERcnBqW9mjy6dSpJyeC8uc3J5/MjzJl4JFhVZpeT3hMDFrz5knWp5rBoBJTT0tmRUZCfPyzk0pPet/JSZJL4qWFhoZSo0YN9uzZ81x3Kt9++23y5cvH7Nmz0y+4l1C3rursd+2aqi31hNqvQgghRJaXVtfwTk5Oqb6+9/T0zBB1NK2SlNLpdIwZM4YxY8Yke69NmzZcvXqVY8eOUbt2bfLkyWOFCIUQIgv6+2+YNAmCglSy5GUfjo5Pfi8mRiWdTp4EPz+4eFElgR7n4ACvvpo8AZU/f/p/H/b2aqjeU+4OCWFtoaGh+Pr6EhAQ8Fyf27ZtG3v37uXixYvpE1ga8PBQHQOPHlVD+N55x9oRCSGEEMLSrJKUepYCBQokGQ4mhBDiJVy5AiNGwIYN1o0jd+7kyady5VTNJiFEirp06UK3bt04cuRIqj8THR3NgAEDmDx5Mjlz5ky/4NKAj49KSu3fL0kpIYQQwhZlyKSUEEKINBAaCl9/DXPnqrpJdnbQpw+0bw96vZoh7lmP1G73+MPBAcqXT5qAKlxYhrIJ8Zx+/PFHvL29GTx4cKo/M378eBISEnBwcGDnzp00adIkw8483KABTJ+uekoJIYQQwvZIUkoIIZ7GYIDr1yFPHsiRw9rRpE5cHHz/vRqq9+CBWvfGGzB1qioULoTINLy9vZ9r++vXr/Pdd99RvXp1rl27xqxZsyhSpAi//vrrExNTj898a6ovodfrnzjhSVqpXRt0OgcuXtRx86aeAgXS9XBPZWprerc5o5L22277bbntIO3PyO3X6/VomobRaMRoNKb5/rX/JtYxHcPWpEX7jUYjmqah1+uTTVCX2t8pSUoJIQSoRM7Fi3D+fNLHpUuq54+zM7z9Nnz4IdSqlTF7/BiNsGYNjBwJN26odZUrw7ffQtOm1o1NCGERS5cuJX/+/OzevRsXFxeGDRtGsWLF2LVrF82bN0/xM5MnT2b8+PHJ1v/xxx8WmaCkWLGGBATkYPZsP+rVu53ux3uWnTt3WjsEq5L22277bbntIO3PiO13cHCgQIECREVFkZCQkG7HiYyMTLd9ZwYv0/6EhARiY2PZv38/iYmJSd6LiYlJ1T4kKZUJREdHJ6vGn5KwsDAuX75MjRo1nrqd0WhMdTf+e/fuYTAYyJs3L6dPn6Zs2bI4OKT+12bt2rWULVuWSpUqpWr7uLg4bty4QenSpVN9DCGey4MHyRNP58+Dv79K6qTE0VHNwLZsmXq89ppKTnXrBqn427SIvXth+HA4dkwtFy4MEydC9+6qoLcQwiYEBgbStGlTXFxcAHB3d6dUqVJcuXLliUmpkSNHMnToUPNyREQEXl5eNG/e/KnTRKeVnTvtmDMHoqKq0qpVlXQ/3pPo9Xp27txJs2bNcHyRGT0zOWm/7bbfltsO0v6M3P64uDhu3ryJm5ub+f+1tKRpGpGRkbi7u6NLwxvOqb1+Dw0N5fr161SrVu2p2z3P9fvzSIv2x8XFkS1bNho0aJDsZ5Tamf0kKZUJvPXWWxQpUoQ5c+awaNEiwh+ZQr1o0aK8++67ABw5coS5c+fy119/cerUKQoWLEjevHmT7e+DDz6gWLFiFC5cmLCwMIYNGwZAYmIiZ86coUqVKuZtV6xYwXfffcfFixf57LPPyJ07NytXrkyyv9jYWIKCgsz/iBkMBjw9PQkNDaVv377s3r071W3ds2cP77zzDkFBQameylKIZDRNzTCXUvLpzp0nfy5nTihbNvmjWDFViXfuXFi7Fk6cgPffV0mgnj3hgw9UwW4rcAsMxP6tt2Dr1v9WuKmeUkOGgAV6OAghMpYiRYpw/vx587LRaCQwMJDChQs/8TPOzs44pzDhgKOjo0UuUBo1gjlz4MABexwdrZ9Et1S7Myppv+2235bbDtL+jNh+g8GATqfDzs4uXZIypiFrpmOklY4dO6bq+v3QoUNMmzbtha/fTerUqcPnn39Ou3btiI+Px8XFhfLly5vfj46OpmzZsmzbtu2J7dfpdBQqVIg///yTsmXLprqtdnZ26HS6FH9/Uvv7JEmpTGDZsmV07tyZX3/9lblz59KrVy9y5crFxYsXWbFiBXXr1mXv3r3kzJnT3Itp9erV/PHHHxw4cCBZ13snJyf0ej0+Pj7UqFGDMmXK4Ovryz///MMbb7zBli1baNCgAQDr16/ngw8+wMHBgXXr1jF69Gji4uJwdnbGaDRib2/PmTNn6NevH7du3cLJyYm8efMyY8YMJk6cSL58+Rg4cCBhYWEAnDlzxpw1vnHjBlWrVsXT0xM3NzdAJbg8PDyoXbu2+R+GyMhIQkNDuXHjRqoyzsKGaBquQUHofvsNLl9Omnwy1VJKSaFCKSef8ud/8rC8WrXUY8YMWLIE5s2Dq1dh9mz18PFRvafatwdLJFSDg7H78ksa/fgjdkaj6g31/vswdqxqhxAiS4uIiCBbtmzJTvg6depE9erV2bBhA7Vq1WL27Nno9XqaZuAhvP+dcnDmDISFqRJ+QgghRGaVmuv3/fv3U6hQoZe6fjf1oHJ2dsbFxQWDwYCzszM6nQ4/Pz/zvvfu3cv333+fLM49e/YwefJkfv/9d+zs7AgNDSWHFWroSlIqE8ifPz9//vkn9vb2TJkyhY4dO3Lw4EGaNGnC6dOnuXHjBqtXr+bDDz80f2bSpElcuXKF7du38+abb+Lk5GRO8pi61Xl7e7N582Yq/Ff4uG7dusycOZPWrVuzZ88e7O3t+euvv7h27Rpz5swhKioKTdPYuHEj0dHRfPLJJ4wZM4YaNWrg5+dHs2bNGDZsGC1btmTAgAEYjUbOnTuHi4sLTZo0YciQIUmSSkWLFuXkyZMUKlQInU5HXFwcFStWZP78+TRt2tT8R+Tv74+Li4skpIQSEQG7d8OOHThs306zmzdT3s7ODl55JXni6dVXX65geZ48MGwYfPIJ7NqlklObN6upo/btUwmh995TCaKiRV/8OE8SEwMzZ8I332D/3/hvo68vdtOmqbYJkQUlJqqyb3FxaiRtZCTcvOlOYqIaXWuLKlWqxKxZs2jXrl2S9WXLlmX16tWMGTOGS5cuUbJkSTZt2pSh/w/Nm1f983z+PBw4AI81SQghhMhUUnP9vnLlSj799FPzZ17k+v2jjz5i2bJlxMXF0a5dO+rVq8f27dvRNI133nnHPCQvODiYbNmyJYtz5cqVVK9e3bydnZ1dku3i4+Oxs7NL9x50kpTK4O7du8f48eOZPHmyOWNqZ2dH9+7d2bhxo3n58TGgOp2OtWvXomka+fPnJ1u2bOZq+BERESQmJrJkyRKMRiNt27Zl9uzZAPTp04fAwEDy5MnD8OHDAdi3bx8lS5Zk3LhxhIaG8r///S9ZnHFxcVy8eJEmTZpgNBqpXLkyDx48oH79+uj1es6cOcODBw8YP348ffr0YcCAAQB069aNIkWKsGTJEqZNm0atWrVo0qQJjRs35v3336dZs2a8+eabtG7dmm+++SZ9vmSRsWmaun2+fbt6/PWXukIFdIDBwQG7smXRlSuXNPlUqhSkw9hzMzs7aN5cPQID4ccfYcECNWxw4kSYPBnefBMGDFDbvGyXYIMBVqyAL76AW7cAMFarxsH27an12WfY2eqVubAqTVOdEoODISREPcLCVO7UlEBK6fl530te7s0RaEzbtnq8vKzQcCswzZBjEhAQ8MRt27RpQ5s2bdI5orTl46OSUvv2SVJKCCHEE2iaOslIK0YjREerEQfPOld3dU3VREeWvH6fO3cuc+fOpWHDhowYMYKWLVtiNBo5ceJEsrjc3d2TLF++fJnVq1fj6urKwoULAVW0vGTJkubY4uLimDlzJv369Xtmu1+GbSel0vqXOrVS+QsNahzm1atXadSoEX/88QcA9vb22NvbP7H46C+//MLKlSsJDw9n2bJlhIaGJnl/wYIFHD58mJ9++inFz3/55Zfs3LmT7du3p1hjwmg0kpCQYO4aCJj/gAYNGgTAt99+y6+//sqSJUsIDAxkxYoVrFixgnHjxhEcHGze12+//cann37KsmXLmDBhAv3796dr164ULlyYjh07MmbMGLp3786oUaNS9X2JLOLBA9ULaft22LHDnIQxK1UKWrYksVkztsfG0rJ9e+uOgS9SBMaPh9GjYdMm1Xvqzz9hyxb1eOUV6N8f+vQBT8/n3/+uXap+1cmTarloUZg8GUOHDoTt2JG2bRE2zWiE8PCHCabHH48mn0JCIDQULD2DtIMDuLho6HQJJCamfX0JYR0+PjB/Puzfb+1IhBBCZFgxMap+ahqxA3KmduOoqFRNcGSN63eTyMhIJk+ezNy5c1N8f9++fVSuXBm9Xk+vXr34/PPPGTJkCB4eHhiNRhwdHbl9+3aKOYD0ZNtJqTT+pU61VP5Cg8po/vrrr3z77bfmukuapuHo6GjOnD6uffv2tG/fnuLFi2MwGNi/fz8tW7bE0dGRhIQEZs+ezc0nDXn6T2xsLOPGjWPChAk0bNgQBwcH7t+/j9FoZPPmzSQmJnLkyBG8vLyIjY1l/PjxVKhQgdq1azN06FCmTZv2xH0/Onufm5sbgwYNwsHBgbVr17JixQoKFChAx44dOXbsGD169OCGaWp7kXVpmkq47NihElEHD5p7QwGQLZuqhPvGG+pRooT6mF6P8bGCfVbl6AgdO6rHhQvqCmvJErh2DT7/HL78Ejp1UrWn6tR5dnL6zBn47DP1nYAadvjFFzBokOoFZulsgMi0wsNV2bUbN56cYDIlmQyG59+/u7sagpUvn8q7Zs8Ozs7q1/Tx55TWpXYbZ2d1M1OvT2Tbth0UKdIq7b8sYRWmulJ+fuq+hBVKWgghhBAvzdLX74GBgYSFhTF48GAaNmxIzpw5GTJkCOPGjePOnTvkyZMHJycnihcvbp5ILDo6mu7du9O3b19i/uukc+/ePRwdHZMlpAz/nRg+Kfa0YNtJqUzCwcGBrl27YjAYMBgMhISEkOe/KqCGZ1w9ODk5Ub9+fR48eEC2bNmIiYnhxo0bjBkz5omfee+99xg6dCht2rRhwoQJ7N2796nD9wYMGMC9e/coX748vXr1YvTo0Tg6OqLX6+nSpQsJCQmEhYVRvXp1bt++bR66d/fuXaZOncrmzZuZMGECCxYsAKBx48Z8//337N+/n1WrVjFmzBiWLl3K8uXL02UqUGEl4eFJe0M9PitemTIqAdWypbqFntl+9q++CrNmwaRJsGaNmrnv2DE1BG/FCqhUSSWn3nlHXdE/6s4dlcD66SfVdcXBQQ0DHDPmxXpaCZsQHa0ST5cvw6VLSZ8fu+H2TDlyqCSTKdFkep3SOk/PzPfnKTKeQoWgZEm4ckWN0n7zTWtHJIQQIsNxdVUdPNKI0WgkIiICDw+PZ8++9xyzWlvq+v2jjz5i0aJFODg4MHHiRD7++GNGjBhhfv/tt9+mS5cu5utv0yinDRs2MGTIEL744gvzzHsJCQno9Xo8PDzMyStN04iOjuaHH36gZ8+eqW7/87LtpFQa/1I/13Gf09ChQ6lYsSKxsbGcO3eOQoUKAWqcp9FofOov96NTaDo5OVGyZEkSExO5dOkSpUuXTrKtv78/mzdvZs6cOeZ1bdq0wcnJiaCgIBITE9mzZw+RkZH8/fffeHl5MWDAAEqXLm2eYc8kJiaGNWvWJBu+ZxIWFsbp06f5559/uHLlCq+++ip58+Zl/Pjx7Nmzh7lz52I0Gjl48CBbt26VhFRmZzSqW+Cm2lCHDyftkuHqCo0bP0xEvfKK1UJNU66uathenz7w779qaN/q1XDqlEpKffYZ9OihXhcvDt9+C9OmPRxa3KGDqk9VqpRVmyEyhvh41fHu3DkdW7eWYMsWe65eVcmn27ef/tlChdSvWL58T080eXpaZgJJIR7n46OSUvv3S1JKCCFECnS6VI84ShWjUV2PZM/+8vVfH2OJ6/fevXszduxYOnXqZF5vfKQQZ1RUFFWqVDEvm47Zt29f+vbtmyQp17t3b3bt2kWpUqXYs2dPsppX6cm2k1Jp/UudTm7cuMGOHTv47rvvGDhwIJMnT6ZevXrky5fPPG1kQkJCip8NCQlh6tSpzJgxI8n6evXqsWvXLkqXLo3BYDB3x5s+fTo9evRI0m1v8+bNT+0pVaNGDc6ePZssKRUaGkrv3r2Jj4/n7t27NGzYkICAAN577z0AihcvztatW3FycqJGjRrmWfwWLlxIuXLlkhynbdu2xMXFSWIqs7l3D/74Q/WE2rED7t5N+n7Zsg+H5NWvn/W7W9SooR7Tp8PSpSpBdemS6kU1d67qMfXfjHrUqqW2q1fPujELi0tMhOvXU+7xdP26qei3A1Ah2Wc9PVX+snRp9Wx6XbKkdUarC/E8GjSARYtUsXMhhBAis7LU9Xu1atWSfd7e3h4XFxdiYmK4evUqFStWBKBIkSJJElaP2rNnD1u2bOHkyZO0b9+e4cOHM23atGf3Hksjtp2UyiS++uorevfuTZEiRQgPD2fx4sXMmjWL+vXr8/vvv9O8eXOaNm1qruZvYjQaadmyJZ06deL69etomkbPnj1p3LgxvXr1YvLkyfTo0YNOnTrxyy+/EBAQwI8//siZM2eeGZPRaCQxMdHctQ9gyZIl7Nixg7t37xIYGMjVq1cJCgri2LFjKfaU6tChA9evX0+yj9jYWIKDg5NkdEFldevXr8+8efNe7EsUlhEUBH///fBx9GjSabOyZ4cmTR72hipe3GqhWlWuXDBkCAwerAqiz5sHv/6qElKvvAJTpqi6VBa8Q2HrDAZVYykwUD1u3Xr4Ojxc/Sjs7NTzo6+f9ZzabY1GVe/p0iXVE+pp5cLc3aFUKSPZs9+mQYOCvPqqvTkJlSuX5b4zIdKaj496PnpUdWSXRKoQQojMKDXX782bN2fHYxMWPe/1e7Zs2ZIde/LkyQCMGDGCxo0bs3btWoKCgti1a1eKHTx27dpFv379WLFiBV5eXmzevJmWLVvSrFkzpk6dmmLiK61JUiqD+/PPP1m2bBmXLl0CYNiwYdSvX5+ePXuSkJBAly5d8PPzw8nJibt375q72d24cYPbt28zadIk7O3tqVSpEt27d6dly5a0aNGCHDlyMGTIED755BMOHjyIs7MzJ06cwNfXl1KPDBMyGAxJlgHz0L7ChQsTGBho3q5Xr158++239OjRgzVr1tC4cWPy5s2b5LOPTmm93VS8+RF+fn74+vri5+f38l+eSF9GI5w9mzQJ5e+ffLvy5ZP2hpJxQQ/pdCpJ16SJyoJcvqwKoFt4xousLiFBDW1LKeFken379osV+E4vLi6qd5Mp2fToc758kJhoYNu2Y7Rq1QpHx/QrPCmEJRUrph7Xr8OhQ9CsmbUjEkIIIZ5Paq/fXVxcuHPnzktdv4eGhnLkyBGuXbtmTjidOnWKiRMncuLECfbt20d4eDijRo2ibNmyzJs3j5YtWwJw7tw5c33npUuX8uZ/4+YLFSrEX3/9xfDhw6lZsyZ16tThs88+o02bNun2nUlSKoN77bXXmDlzJsWLF+fQoUPs3LmTgwcPAqog+cWLF4mJiWHZsmUMGTKEr776CsCc5WzVqhWxsbEMHDgwWWZ00aJF+Pr68u6772JnZ0e3bt3o0qVLkm3i4uK4fPkyJUuWTLLeaDSif+RWfoECBcx/ULNmzaJp06asXr3aHEujRo04dOgQa9asMceYkvj4eOLj41/w2xLpKjoa/vnnYQLq0CE1RdKjdDqoWFENOatXT43F8PKyTryZTeHC6iGeS2ysPRcvqpGhKSWbAgNVD6jUsLODggWhSBH1KFxYPf9XlxJNUw+jMXXPz7tt4cIPk09FiqR5aQMhMgUfH1i2TA3hk6SUEEKIzCa11+8rVqzg448/5uuvvwZe7PrdxcWF0aNH4+vrS61atfjqq6+YNm0a/fr144cffiBnzpwULFiQX3/9lRUrVjBv3jxatGjBmTNnqFGjBm+99Rb79u2jfPnySY7j4eHBggUL6N+/P1OmTKF4Oo9ukaRUBpcrVy4GDhwIQJ06dbh8+bL5l1On0/Htt98CqshZ27ZtzQXUdDodrVqpqbJT6tYH0KRJE+7evYv7IzN/PT5uVP+EMSR2dnZJ6k69+UhF0jx58nDixAnzcvny5SlfvjwxMTEcOnSI3LlzP7G9tWrVIiQk5InvCwu6fTtpLyg/P1Xs5lHZs6vaR6YkVO3aMo+3SDf37qm86JEjqk7+0aMOhIb6puqzzs4Pk0yPJpwefZ0/v5roUAhhPQ0aPExKCSGEEJnN81y/9+jRA7f/xqq/yPW7m5tbkuvuESNG8MknnyS5vjfp3r073bt3B6BixYoEBgaSO3duIiIintiWatWqsX79+udq/4uQ0+9M5kmFvl1cXMy/0M/Dw8PjZUNKNVdXV1xfYOZBYQEGQ/KheAEBybcrXPhhAqpePahcWa7iRbpISFATFB458jAJdfny41up3pkeHhpFiuhSTDQ92ttJSnQJkfGZ6kr98w/ExsITzsuFEEKITOFp1+8v4mnX705OTknqNT+Np6fnEwufW5pcTQphi6Kikg/FezxLbmcHlSqp5FPduuq5aFG5shdpTtNUDZlHE1DHj0NKI3lLlVKd82rVgmrVErl+/Xc6dGiOo6Oj5QMXQqS5EiWgUCHVWffIEWjY0NoRCSGEECI9SVJKiIzi5k3YskV1B0lMVNNv6fUpv35knb1eT4PQUBzGjk3x/RTXPVJw3szNTQ2/M/WCqlULLNiTTtiOiAj499+HCagjR1Ku+5Qr18MEVO3aULMmPDr6V6/XCA5OTP5BIUSmpdOpIXxr1qghfJKUEkIIoaV07SIyhLT42dhcUkp+obMe089Ul9l68GganDsHv/6qHkePvtBu7IAXmgXeyyvpULyKFWUonkhziYlqZOijvaDOn0+eF3VwgCpVHiagatVSs89ltj9rIcTL8/FRSan9+60diRBCCGuyt1czDCckJDyxzpKwrpiYGICXGrVgM1egpi8pJiZGfqGzGL1ej6Zp5n+0MjSDQV2VmxJRV648fE+nU8Pk6tZVVZkdHdWV+qPPKbxO1Ok46udH9Tp1cMiWLeVtH1/n4qK6oQiRBjRNTcT46Ix3ly6pJNTRo2rixscVL560F9Rrr6lfSyGEMNWVOnRI1ZdLZXkMIYQQWYyDgwOurq6EhITg6OiYbFKul2U0GklISCAuLi7N950ZvEz7NU0jJiaG4OBgcubM+VLX4jaTlLK3tydnzpwE/zdGxNXVNfP1rHkKW/2DMhqNhISEEBMTk3GTUnFx8Oef8MsvsHlz0nFKTk5qzut27aB1azX913PS9HruOjqiNW+ukk5CpCGjEUJDkyacTI9H16WUeDJxd1dD70xJqFq1XuhXXQhhI159FfLmhZAQNdS3Xj1rRySEEMIadDodBQsWxN/fn+vXr6f5/jVNIzY2lmzZsmWp3EBqpUX7c+bMSYECBV4qDptJSgHmLys4peIlmZwt/0HpdDoePHiQsdp9/z5s26Z6Q23frgqLm+TIAb6+KhHVooW6YhfCChITISgoecLp0eVbt1RPhdTInfvhbHfFikGNGqoX1Kuvqrr5QgiRGqa6Uhs2qCF8kpQSQgjb5eTkRKlSpUhI7Qnpc9Dr9ezfv58GDRrY5KQ5L9t+R0fHNOkYYlNJKVOmNV++fOj1emuHk6Zs+Q9Kp9Nx8eJFa4ehrt43bVKJqD171BW/SaFCKgnVrp0alyBjEUQ6i4tTv5KmBNPjz4GBcOeO6gmVGvnzq2TTow9TAsr02tU1fdskhLAdPj4qKbVvH4wcae1ohBBCWJOdnR0u6VDnwd7ensTERFxcXGzuGhoyTvttKillYm9vn3GHer2gjPILZQ1WSzBqGly48LA+1D//JH2/XLmHiahq1aSriEgTj9ZvCgjQsWtXUY4ftyMoKGnSKSwsdfuzt1c505QSTaZHwYKSRxVCWFaDBur577/VPR6Zh0MIIYTImuS/eCGeh9GoqjebElGXLj18T6eDOnVUEqptWyhd2kpBiszKaFQlx57Wu+nWrUfrNzkArz1xfy4uSRNNhQurh5fXw+X8+VViSgghMpKKFdV8HOHhcOKEGg4shBBCiKxHklJCPIvRqG7VrlyphucFBT18z8kJmjRRiag2beAli7wJ22A0wvXrcPo0nDmjnk+fVjnO1Hb8y5kTChfWcHYOpnLlvBQtapck+VSkiLqgy0il1oQQIrXs7OD119X8IPv2SVJKCCGEyKokKSXEk1y5AsuXq4e//8P1Hh7w5psqEdWypVoW4gnCwh4mnUyPM2eS1r5/lE6ncpuPJ5ge7+2UPTvo9Yls23aYVq1a4egow0OFEFlLgwYPk1LDh1s7GiGEEEKkB0lKCfGo8HBYtw6WLYODBx+ud3eHTp3g7behYUMpsCOSiY2Fc+eSJ5/u3El5e0dHKFsWKlRQw1QqVoTy5VXCycbKwgkhRIp8fNTzgQNgMMhQYyGEECIrkqSUEHo9bN+uekRt3gym6Ubt7KBFC3j3XTU0T6YWE6gLo2vXkvd+unLlyTPZFS/+MPFkepQuLcknIYR4mipV1D2hBw/Uv7NVqlg7IiGEEEKkNUlKCdukaXDsmOoRtXo1hIY+fK9SJejZE7p2VdOOCZsTF5e0yPjNm2qixdOnVW+o2NiUP5cnT9LEU4UK6uHubtn4hRAiK3BwgHr1YMcONYRPklJCCCFE1iNJKWFbAgNhxQqVjDp//uH6/PnhnXdUr6jKla0Xn0h3kZFJZ7N7dFY70+tHc5QpcXGBcuWS934qUEAKiwshRFry8VFJqf37YfBga0cjhBBCiLQmSSmR9UVFwS+/qETU7t2qlxSozEK7dioR1ayZuiUrMi1NUyXBnpZsCgyEiIjU7c/FRRUWNz1KlHiYfCpRQmqbCCGEJZjqSu3fr/6dl8S/EEIIkbXIVbjImgwG2LtXJaI2bIDo6Ifv+fhAjx7QsSPkyGG1EMWL0TRVv+mff+DIETv27q3L8OEOBAaqYXep4eGRNOH06Ox2pkeuXHLxI4QQ1latGmTLpnqwnjunJoQQQgghRNYhSSmRtZw7pwqWr1ihusWYlCqlekR1766qTotMQdNUT6d//334OHoU7t83bWEP5E3yGU/PpyebCheWGk9CCJFZODlB3bqqo/P+/ZKUEkIIIbIaSUqJzO/ePby3bsX+669V8XKTXLmgSxeVjKpVS7q9ZAJhYSrp9M8/D5NQQUHJt3N2htdeg2rVDNjbn6Rt20oUL+5AoUJq2J0QQoisw8dHJaX27YMPP7R2NEIIIYRIS5KUEpnb3r04dOlCpbt31bKDA7RqpRJRvr4qeyEypKgoOH48aS+oa9eSb2dvr+6M16jx8FGxIjg6gl5vZNu2m7z+ekUcHS3fBiGEEOmvQQP1vG+f1JUSQgghshpJSonMSdNg6lQYNQqd0Uhk4cK4Dh+O/TvvQN68z/68sKj4eDh1KmkC6vx5MBqTb1uqVNIE1Guvgaur5WMWQgiRMdSqpe4xBQWpmoKlSlk7IiGEEEKkFUlKiczn/n3o1Qs2bQLA2L07+1q3pkX79thLd5kM4cYNdUf78GGVgDp5EhISkm9XuLBKPNWsqZ6rVVOjLoUQQggTFxeVmNq/X/3fIkkpIYQQIuuQpJTIXPz81Kx5V6+q6qezZ2Po1QvD9u3WjsymBQSoyQ737VMPf//k2+TOnbQHVI0aULCgpSMVQgiRGTVo8DAp9d571o5GCCGEEGlFklIi81i8GAYMgLg4NYPezz+rrjV6vbUjsymappJOpiTU3r2qZ9Sj7O3Vj6Z+/Ye9oLy9pQ6IEEKIF+PjAxMmqMSUEEIIIbIOSUqJjC82FgYNgkWL1HKrVrB8uep6I9KdpqkaHqYE1L59EBiYdBsHB6heHRo2VBcO9eqBu7s1ohVCCJEV1amj/q+5cUP1zi1e3NoRCSGEECItSFJKZGzXrkGHDmrYnp0dfPUVjBypXot0oWlw6VLSJNTt20m3cXRUPaB8fFQiqk4dcHOzRrRCCCFsQfbs6ubH4cPq/yVJSgkhhBBZgySlRMa1eTO8+y48eKBm1Fu9Gpo0sXZUWY6mwYULSWtCBQUl3cbJSRWZfTQJJTPiCSGEsCQfH5WU2r8feva0djRCCCGESAuSlBIZT2IijBkDU6ao5Tp1YN06KFLEunFlEZoG584lTUIFByfdxtkZatd+OByvdm3Ils0a0QohhBCKjw988436f0sIIYQQWYMkpUTGcvcudO0Ke/ao5cGDYepU1VVHvLCwMNi1C3bsgD/+SD4cz8VF5f5MSahatdQ6IYQQIqOoV0+N3r96FW7dgsKFrR2REEIIIV6WJKVExvHXX9C5M9y5owoULVqklsVzS0yEf/6B339Xiah//1U9pEyyZVMn9z4+6lGzpuodJYQQQmRUHh7w2mtw7Jgawte1q7UjEkIIIcTLkqSUsD5Ng5kz4bPPwGCAcuVgwwZ49VVrR5ap3LwJf/6pElG7dsH9+0nfr1ABWrSAli2hfn3pCSWEEJlJaGgoNWrUYM+ePRR/jirfer2eqlWrMnv2bBo2bJhu8VmKj49KSu3bJ0kpIYQQIiuQpJSwrogI6NNHJaEAunWDH36QqdxSITYWDhyAbdvs2LixETdvOiZ5P1cuaNZMJaKaN5eSXEIIkVmFhobi6+tLQEDAc3926tSpnDlzJu2DspIGDWDGDKkrJYQQQmQVkpQS1nP6NHToAJcvg6MjzJoFH34IOp21I8uQTLPkmYbk7dsHcXEA9oAHdnYaNWvqzL2hatQAe3srBy2EEOKldenShW7dunHkyJHn+tzly5f59ttvn6tnVUb3+uvqNOHCBTVJR7581o5ICCGEEC9DklLCOpYvh/79VXcfLy9Yv15V1xZJ3L8Pu3c/TETdvJn0/cKFoVkzI56exxg+vAr58zumuB8hhBCZ148//oi3tzeDBw9+rs/179+fESNGsH379nSKzPJy54aKFeHUKVVXqmNHa0ckhBBCiJchSSlhWXFxMGSIGqIHalzZypXg6WnVsDIKo1HVytixQyWiDh9WZbZMnJzU0IWWLdWwvPLlITHRwLZtt8mdu4rV4hZCCJF+vL29n/szixcv5sGDBwwfPvyZSan4+Hji4+PNyxEREYCqR6XX65/72Omtfn07Tp2yZ88eA23bGtNsv6a2ZsQ2W4K033bbb8ttB2m/LbffltsO6d/+1O5XklLCcgIC1C3NY8dU3/svv4QxY2SMGWrCwYULYcECCAxM+l6ZMg8LlPv4gKurdWIUQgiROYSEhDBy5Eh+//137FPxf+zkyZMZP358svV//PEHrhnwPx03t4JATbZti6J5871pvv+dO3em+T4zE2m/7bbfltsO0n5bbr8ttx3Sr/0xMTGp2s4qSaklS5bQu3fvZOsXL16Mt7c3H3zwASEhIYwaNYqhQ4daIUKR5rZtg+7dITxc9b1fuVJlWWyYpsGePTBvHvz6KyQmqvXu7tC0qUpEtWgBWagUiBBCCAsYMmQIffv2pXLlyqnafuTIkUnOtyIiIvDy8qJ58+Z4eHikV5gvrHp1mDoVrl/3oHbtVuTOnTb71ev17Ny5k2bNmuHoaHvD4aX9ttt+W247SPttuf223HZI//abel4/i1WSUt26daNdu3bm5aioKF577TXKli1L8+bNGTZsGF27dqVLly689tprNGrUyBphirRgMMD48fD112q5Rg1VP6pYMevGZUXh4bB0KcyfDxcvPlxft66q896xI7i4WC8+IYQQmduqVatwd3dnzpw5gDrP8vX1ZfTo0YwYMSLZ9s7Ozjg7Oydb7+jomCFP0gsXhldfhQsXdBw+7Ejbtmm7/4zabkuR9ttu+2257SDtt+X223LbIf3an9p9WiUp5eTkhJOTk3l57ty5tG/fnkOHDlGoUCHGjBmDTqfjyy+/ZNGiRZKUyiw0TU2F4++vhuoFBMD27aoSKcCAAWoe5xROfG3B0aOqV9Tq1aq+O4Cbm+pA9uGHUKmSdeMTQgiRuURERJAtW7ZkJ33+/v5Jlrt06cKQIUNomYV6KPv4qBn49u8nzZNSQgghhLAcq9eUiouL47vvvuPIkSOMHz+eRo0aodPpAKhZs2aKd/RMMlthzvRkkSJtmgb37qH7L+GkCwiA69fRPfralG159GOurhjmzUPr2tUUbJqGlZEL1MXEwLp1On74wY5jx+zM6ytU0Ojf30i3bkbc3dW6Fw0/I7c/vdly20Hab8vtt+W2Q8YpzGltlSpVYtasWUl6nwMUf2zct4uLCwUKFCBnzpwWiy29+fioOVP27bN2JEIIIYR4GVZPSq1atYpatWpRvHhxIiIiKFeunPk9Dw8Pbt++/cTPZrbCnJbwskXKHKKicA0OJntwMNmCg8l+96752TU4GIe4uKd+XtPpiM2Th5h8+YjNl4/o/Pm51aABUTlyqLpS6SgjFai7dcuNHTuK8+efXkRHqzvYDg4G6ta9zRtvBPDqq/fQ6eDAgbQ7ZkZqv6XZcttB2m/L7bfltoP1C3NamqZpSZYDAgJS9bm9e/emfTBW1qCBej5xAh48gBw5rBuPEEIIIV6M1ZNS8+fPZ9y4cQA4ODgkqWng4uLy1BPDzFaYMz09d5GywEDsNm1SPZ78/dFdv656Ot2//8yPagULohUrBsWLoxUrhubtDcWKoRUvDl5eODo5kQMwnR+WeIl2pUZGKVCn18PmzToWLLBjz56HvaK8vTXee89Ir15G8uYtABRI4+NmjPZbgy23HaT9ttx+W247ZJzCnMJ6CheGEiXg6lX4+29o1craEQkhhBDiRVg1KXXlyhWuXLlCs2bNAMidOzchISHm9yMjI5PUnnpcZivMaQmpavupU9CkCYSGpvx+3rzg7a2mfXv8uWhRdNmyoUvjuNOCtX7ugYGwYAEsXAh37qh1dnbw5puqVlSLFjrs7OyBZ0/L/TLk99422w7Sfltuvy23HaxfmFNYl4+PSkrt3y9JKSGEECKzsmpSat26dfj6+ppP/mrUqMGqVavM7584cYLChQtbK7ysyc8PmjaFsDAoVw7eeCNp0ql4ccie3boxZgJGI+zapQqXb9miJhkEyJ8f3nsP3n8fiha1boxCCCFEVtagAfz0k9SVEkIIITIzqyalduzYQa9evczLbdq0YeDAgezatQsfHx+mTp1KixYtrBdgVnP8ODRrBvfuQc2a8PvvkIWKnlpCWBgsXgzz56u7syYNG6peUe3awVM69wkhhBAijfj4qOejRyE6Wu6pCSGEEJmR1ZJSsbGxHDlyhAULFpjXeXp6MnPmTFq1aoWbmxs5c+ZkyZIl1goxazl2TPWQun8fateGHTukKuhz+Pdf+N//YO1aME346OEBPXvCBx+oTmdCCCGEsJz/qgpw4wYcOqROc4QQQgiRuVgtKZUtWzbiTVf3j/jggw9o0aIFFy5c4PXXX8fNzc0K0WUx//6rekg9eAB168L27SqjIp4qIQE2bIDvv4fDhx+ur1pV9Yrq2lXuygohhBDW1KABrFihhvBJUkoIIYTIfKw++15KvL298fb2tnYYWcPhw9CiBUREQP36sG0buLtbO6oM7e5dVbh83ryHhcudnKBLFxg4EGrUAF1GrPQuhBBC2Bgfn4dJKSGEEEJkPhkyKSXSyMGD0LIlREaqW4m//QbS8+yJjh1TvaLWrFG9pAAKFlS9ot5/XxUxF0IIIUTGYaordeQIxMWBi4t14xFCCCHE85GkVFb1119qZr2oKGjUSE0RJ2PNktHr1RC92bNVDs+kdm34+GPo0EEKlwshhBAZVcmSUKAABAWpxJQpSSWEEEKIzMHO2gGIdLB/v+ohFRUFTZrA1q2SkHpMcDBMmKCKpHbtqhJSjo7Qowf8848qmNq1qySkhBBCiIxMp3uYiJIhfEIIIUTmIz2lshjdvn3Qti3ExKji5ps2QbZs1g4rwzh+XA3RW7364RC9AgUeDtErUMC68QkhhBDi+fj4qNlx9++3diRCCCGEeF6SlMpCPE+dwn7yZIiNVcXNf/lFElKoIXq//KKSUX///XB9rVpqiF7HjtIjSgghhMisGjRQzwcPqhtO8n+6EEIIkXlIUiqL0O3eTe0JE9AlJECrVqpQko1X+wwJeTiL3q1bap2jI3TuDIMGqaSUEEIIITK3cuXA0xNCQ+HoUahb19oRCSGEECK1JCmVFfz+O/bt26NLSMDYqhV2GzeCs7O1o7KaEyceDtGLj1fr8ueHDz6A/v3VjHpCCCGEyBp0OtVbauNGNYRPklJCCCFE5iGFzjO77duhbVt0cXHcqVkTw9q1NpmQSkzU8fPPOl5/HapWhSVLVEKqRg1YsQKuX4dx4yQhJYQQQmRFpiF8UuxcCCGEyFykp1RmtnUrdOgACQkY27bl3+7decPGElJxcfD993ZMndqMsDD16+zgoIboffyxDNETQgghbIFpBr6//oLERHUuIIQQQoiMT3pKZVabN8Nbb6mKnh07Yli1Cs3R0dpRWYzBoHpDlS4Nn39uT1hYNvLl0/jyS9UrauVKSUgJIYQQtqJiRciZE6KiwM/P2tEIIYQQIrUkKZUZ/fqrmjJOr1ddglatUhW8bYCmwW+/QZUq0Ls33LwJRYpoDBx4gqtXExk/HgoVsnaUQgghhLAke3uoX1+9liF8QgghROYhSanMZsMG6NRJJaS6dlVdgmwkIXXkCDRqBL6+cOaMuiM6dSqcPZtIs2Y3bLGUlhBCCCH+YxrCJ0kpIYQQIvOQEfeZyfr1KhFlMMA776jxazZQNOHSJRg1SuXjQNVxHzwYRoyAXLlUfk4IIYQQts2UlDpwAIxGsJNbr0IIIUSGJ/9dZxZr1jxMSL37LixdmuUTUnfuwIcfQrlyKiGl06khe5cvwzffqISUEEIIIQTAa6+Bmxvcvw+nT1s7GiGEEEKkhiSlMoOVK1XPKINBZWV++kkVT8iiIiLgyy+hZEmYP18129cXTp1STffysnaEQgghhMhoHBygXj31WobwCSGEEJmDJKUyuuXLVc8ooxHeew8WLsyyCamEBPj+eyhRAr7+GmJioHZtdWK5ZQtUqGDtCIUQQgiRkZmG8O3fb904hBBCCJE6WXv8V2a3ZAn06aOmnOvfH+bOzZIFEoxGWLsWRo+Ga9fUutKlYfJkaN9eDdsTQgghhHiWBg3U8/796vRJziGEEEKIjC3rZTiyip9+epiQ+vDDLJuQ2rULatSAbt1UQqpAATVk78wZeOstOZkUQgghROrVqAHZskFICJw/b+1ohBBCCPEsWS/LkRX8+CP07asSUh99BHPmZLmE1IkT0Lw5NGsGx4+Du7sasnfliuoU5uho7QiFEEIIkdk4OUGdOuq1DOETQgghMr6slenICo4ehfffV68HD1ZFlrJQdyF/f1WzvWpV2LlTJZ8GD4arV9XwvezZrR2hEEIIITIz0xA+KXYuhBBCZHxSUyqjWbJEPbdvDzNnZpmEVEgITJyoRiHq9Wpdt26qd9Qrr1g3NiGEEEJkHaZi5/v2SV0pIYQQIqOTpFRGkpgI69ap1++/nyXOoqKjVW5t6lSIjFTrmjWDb76B116zbmxCCCGEyHpq1VLD+O7cUT2xS5a0dkRCCCGEeBIZvpeR7N6tuhR5ekKTJtaO5qUdPAilSsGYMSoh9dpr8Mcf6iEJKSGEEMIGRUbC7NnQq1e6HSJbNqhZU72WIXxCCCFExiZJqYxk9Wr13Llzpq/0/dtv0LSpukvp7Q2rVqlyWc2aWTsyIYQQQliNwQDDhsHSpek6PZ7p3t7cuWA0ptthhBBCCPGSJCmVUcTGwsaN6nXXrtaN5SUtXQpt26omtWoFp0+rJmWxCQSFEEII8bxy5nx4h2r9+nQ7zMCBkCOHmuF3+fJ0O4wQQgghXpKkCTKKbdtUl3YvL6hb19rRvLBp01SPfIMB3n0Xfv1VZtQTQgghxCM6dVLP6ZiUyptXzeoLMGqUqnEphBBCiIxHklIZhWnoXibtUmQ0wvDh8NlnavnTT9VEgpl8FKIQQggh0lrbtuoE4cwZuHAh3Q4zaJCa4ff2bXXTTAghhBAZT+bLfmRFERGwdat6nQmH7un10LMnTJ+ulqdNU7PtZYHJA4UQQgiR1nLlUoUnIV17Szk7q/MRUM+Bgel2KCGEEEK8oOdOSoWGhrJ3717WrVvHtm3bOHPmTHrEZVt++QXi4+HVV6FyZWtH81yio9UNzxUrwN5e1ZMaPtzaUQkhhBAiQ7PAED6At96C+vVVncsvvkjXQwkhhBDiBaQ6KfXXX3/RpEkTmjZtysqVK/Hz82PHjh18/vnnvPrqq0yaNImEhIT0jDXrMg3d69YtU3UvCgtTs9ts366mX968WdWREkIIIYR4qrZtwcFBzYZy8WK6HUangxkz1Otly9RMwEIIIYTIOByetUFCQgIDBw4kICCA6dOnU6VKlWTbREdHM336dGrUqMGaNWsoW7ZsesSaNQUHw65d6nUmGrp34wa0aKFKQeTKBb/9BnXqWDsqIYQQQmQKuXOrIXw7dsDPP6drN6YaNaBHDzUL39ChsG9fproHKIQQQmRpz+wpNWfOHIoVK8bOnTtTTEgBZM+enS+//JKffvqJzz//PK1jzNrWr1dT1VWvDiVLWjuaVDl3DurVUwmpIkXgr78kISWEEEKI52ShIXwAkyapXt0HDsDGjel+OCGEEEKk0jOTUp988gmjTXPqpiAmJoZbt24RHR1NtWrV2Lx5c5oGmOU9OnQvEzh0SNVmCAyEsmXh4EEoV87aUQkhhBAi02nXTg3hO3kSLl9O10MVKaJmBgY1U3B8fLoeTgghhBCplKqaUuHh4dy8eTPZ+k8//ZTKlSvzzjvvUK1aNfr375/mAWZp16/D33+rPuRvv23taJ7pt99UDanwcKhdW91t9PKydlRCCCFE1hYaGoq3tzcBAQGp2n7BggUULFgQR0dHfHx8uHPnTvoG+KJy51YnFmCR3lKffgoFC8K1azB7drofTgghhBCpkKqklIuLCzt37mTcuHH88ccf5vXbtm1jwIABjB07lqFDh7J169Z0CzRLWrNGPfv4QKFC1o3lGZYtUzVJY2PhjTdUGaw8eawdlRBCCJG1hYaG4uvrm+qE1F9//cWYMWNYvnw5/v7+aJrG8Iw8La4Fh/C5ualhfABffw0hIel+SCGEEEI8Q6qSUtmyZaNPnz6MGzcOd3d3xo8fz5w5c5g/fz537txh1apVXL58WZJSzyuTDN2bNg169lSlr3r0gE2bIHt2a0clhBBCZH1dunSh23OcJ1y+fJkffviBpk2bUqRIEXr37s2JEyfSMcKX1LYt2NuDnx9cuZLuh3v3XXjtNYiIgHHj0v1wQgghhHiGVCWlAHbv3k3nzp0ZM2YMJ0+eBODEiRNkz56dfv36MW3aNF577bV0CzTLOXdO1VBwdIQOHawdTYqMRhg+XNVeAPV6yRIVshBCCCHS348//sjHH3+c6u179+5Nu3btzMsXL16kVKlS6RBZGvH0hMaN1WsL9Jays4MZM9TrH35Qp2NCCCGEsB6H1G7YpUsXFi5cSOXKlQkKCqJhw4aEhITg5ubG9u3bGTt2LCVLlqRHjx7pGW/WYeol1aKFqqmQwej10Levmj4ZVG+pjNz7XwghhMiKvL29X/iz9+7d44cffmDVqlVP3S4+Pp74Ryp/R0REAKDX69Hr9S98/NTSvfUWDjt3oq1bR6IFTjbq1YM2bezZvNmOYcOMbN5sADC31RJtzoik/bbbfltuO0j7bbn9ttx2SP/2p3a/qU5KDR48mCFDhqDT6dA0jb59++Lu7g5Aq1ataNWqFWFhYS8Wra3RtAw9dC86Gjp3hm3bVI/6n35S3d2FEEIIkXkMHDiQunXr8sYbbzx1u8mTJzN+/Phk6//44w9cXV3TKzwzp+zZaWFnh52fH3sXLSKmYMF0P2bLltnZtq0xO3bYMXHiYV577WGBqZ07d6b78TMyab/ttt+W2w7Sfltuvy23HdKv/TExMana7plJKaPRiJ2dHaNHj2b06NFP3TZPnjzm7cVTHD0KV6+Cqyu0aWPtaJIICwNfXzh8GLJlg59/hlatrB2VEEIIIZ7H0qVL2bNnj7nkwtOMHDmSoUOHmpcjIiLw8vKiefPmeHh4pGeYDy1dCrt30zgsDGPfvhY55MWLGt99B+vX1+HzzxPRND07d+6kWbNmONpgrQK9Xtpvq+235baDtN+W22/LbYf0b7+p5/WzPDMpNX36dC5dusT8+fOxt7d/6rZbt27lm2++4cCBA6mL0laZutG3aZOhKobfvKlGE54/D7lywW+/QZ061o5KCCGEEM/j6NGjDBo0iM2bN5M/f/5nbu/s7Iyzs3Oy9Y6OjpY7Se/cGXbvxn7jRuy/+MIihxw7VpUpOHdOx7JljvTpo9ZbtN0ZkLTfdttvy20Hab8tt9+W2w7p1/7U7vOZXZo+/fRTvLy8qFq1Kps3b8ZgMCTbxt/fnz59+vDNN9+wdu3a54/WlhgMYPqOMtDQvXPnoG5dlZAqUgT++ksSUkIIIcSLmD9/Pkaj8anbJCQkULJkyRc+RkRERIq1GoKDg2ndujWfffYZ1atXJyoqiqioqBc+jsW0b69qBhw/DteuWeSQuXI9nIFvzBh48MAihxVCCCHEI1JVU+rLL7/k7bffZtKkSXzyySe88sor5M+fn6ioKK5evYqHhwfvv/8+P/30U3rHm/nt3w937qgzoRYtrB0NAIcOwZtvQng4lC0Lv/8OXl7WjkoIIYTInCZNmkS/fv3YuHEjERERKZY10DSNxMTEFz5GpUqVmDVrVpKZ9gBWr15NUFAQY8aMYcyYMUmOl6HlzQsNG8Lu3WoWvs8/t8hhP/gA5syBixfhm2/sqF/fIocVQgghxH9SXei8TJkyLF26lMTERG7cuEFwcDDZs2fHy8uLnDlzpmOIWYxp6F6HDuDkZN1YUEP0OnWC2FioXRu2boU8eawdlRBCCJF5OTg4YG9vz+TJk6levTpr1qyhS5curF+/nk6dOpmfn6cG5+NJpYCAgBS3Gzx4MIMHD36Z8K2nUyeLJ6UcHeHbb6F1a/j+eztKlUr/wu5CCCGEeOi5K5I7ODjwyiuvULt2bSpWrCgJqeeRkAAbNqjXGWDo3rJl0LatSki98Qbs2iUJKSGEEOJlxMXFmV/rdDrmzZuHp6cn8+bNo2DBgkmeM3zvJUtr3x7s7ODYMYsN4QPVW7xpU0hI0LFsWTmLHVcIIYQQL5CUEi/h99/VGLmCBaFBA6uGMm8e9OypSlz16AGbNmWomutCCCFEphMdHU3u3LkJDAykbt26XL58GVDJqZSexWPy5VND+EBN/2shOh1Mnw52dhp//12Ygwfl5yOEEEJYiiSlLMk0dO/tt1UxTyu5fRtMMz8PGwZLlqju60IIIYR4cU5OTmzevBlPT08GDhwovclfRKdO6nn9eosetlIl6N1b9Vz79FM7nlGnXgghhBBpRJJSlhIdDZs3q9dWHrr39dcQF6dm15s2TfWUF0IIIcTLcXR0pGnTpri4uPDOO+/g6enJggULiIiIYMGCBYSHhyd5lh5TKXjrLXVicvQo+Ptb9NBjxxpwcUnk33/tWL3aoocWQgghbJakIyxl82aIiYESJaB6dauFcfUqLFyoXk+ZorqsCyGEECLtJCYmYjQaad68OX///TdvvPEGhw4domnTpkmepaZUCvLlAx8f9dqCQ/gAChSAjh0vATBihDptE0IIIUT6SvXse+IlmYbudetm1UzQl19CYiK0bGn1slZCCCFElrNlyxa++uorACZOnPjE7RITE/Hy8rJUWJlLp06wZ48awvfppxY9dOvWV9m/vyw3buiYMQNGj7bo4YUQQgibI0kpS7h3TxU5B+ja1WphnDqFuTv6pElWC0MIIYTIksLDw+nZsyf58+cnJiaGAgUKPHHbhIQEPvjgAwtGl4m89RZ89BH8+y9cvw7Filns0M7ORiZONNCjhwNTpkDfvmp+GiGEEEKkD0lKWcKGDaDXQ+XKULas1cL44gvQNOjcGV57zWphCCGEEFlSrly5uHPnDps2bWLixImcOXOG/PnzU7169WRD9QwGA3q93kqRZnD586vu3Hv3qiF8w4ZZ9PCdO2vMmQOHD6ueUosWWfTwQgghhE2RmlKW8OjQPSv5+2/YulVN+vf111YLQwghhMjSnJ2d6dy5MydPnmTBggXExsaSM2dO1q5dy5YtW8yPbdu2sXPnTmuHm3FZaRY+UFUWZs5UrxcvBj8/i4cghBBC2AyrJ6U+//xzWrdubV4+c+YMNWrUIFeuXHz66aeZvwjorVuwb5963aWLVULQNBg1Sr3u3RtKl7ZKGEIIIYRN6du3L35+flSrVg1XV1drh5O5vPWWyg4dOQI3blj88LVrq9M2TYOhQ9WzEEIIIdJeqpNSf/zxB0CSJJG/vz96vZ6YmBhq1qz53Ac/deoUc+fO5bvvvgMgPj6e1q1bU61aNY4ePcq5c+dYsmTJc+83Q1m3Tp3J1KsHRYtaJYTff4f9+8HZGcaOtUoIQgghhE0qVqwYgwcPtnYYmU+BAvD66+q1hWfhM5kyRZ077dkDW7ZYJQQhhBAiy0t1UqpXr16cOHGCBg0aoNfrMRgMdOjQgV9++QVnZ2fi4+Of68BGo5H333+fTz75hFdeeQWA7du38+DBA2bMmEGJEiWYNGkSizL7QH4rD90zGh/2kho4EIoUsUoYQgghhM1KTEzkvffeS7Ju2rRpLF682EoRZRJWHMIHqr760KHq9fDhkJBglTCEEEKILC3VSak8efJQoUIFChUqxNtvv82iRYsoU6YMnTt3xt7eHnt7++c68Pz58zl9+jTFixdn8+bNJCQkcPLkSWrXrm3u4l6pUiXOnTv3fC3KSC5fhqNHVSEn04mVhf38M5w4Ae7uMHKkVUIQQgghbE58fDw9e/YEwN7enjVr1iR5f+HChZw9e9YaoWUeHTqoIXyHD8PNm1YJYcQIyJdPndLNm2eVEIQQQogsLdWz72XLlg1HR0dWrlzJjh078PHxwc3NjSlTpjBixIjnqv0UFRXF2LFjeeWVV7h+/TrLly9nwoQJvP7663h7e5u30+l02NvbEx4eTq5cuZLtJz4+PkkPrYiICAD0en2GmNHGbuVK7AFjkyYYcuZUM/ClE1N7H213YiKMHu0A6BgyxECOHMb0DMFqUmq7LbHl9tty20Hab8vtt+W2Q/q3Py32a29vz6ZNmwB1PpMtWzbze7/88gt37txh+PDhL32cLK1gQahfHw4cUHfZPvnE4iF4eMCECfD++zB+PPToAblzWzwMIYQQIstKdVLKZNCgQYwZM4atW7fSs2dPcz2o57Fx40aio6PZs2cPnp6eJCYmUrFiRX766Sd69+6dZFsXFxdiYmJSTEpNnjyZ8ePHJ1v/xx9/WL+gqKbReNEi3AG/smW5uW2bRQ776Ew+O3cW5fLl1/DwiKdcuV1s25ZokRisxdZnMbLl9tty20Hab8vtt+W2Q/q1PyYm5qX34eDggKOjo3lZp9MBcP/+fT799FO+/vprChQo8NLHyfI6dVJJqfXrrZKUAujTB2bPhtOn4auvYNYsq4QhhBBCZEmpSkodO3aM+Ph4jEYjHh4e+Pr6snjxYnM9qGvXrqHX6/H39ycuLo6yZcs+dX+BgYHUrl0bT09PFYSDA5UqVeLChQuEhIQk2TYyMhInJ6cU9zNy5EiGmgb7o3pKeXl50bx5czw8PFLTtPTj54djYCCaiwsVx4yhYjrHo9fr2blzJ82aNcPR0ZG4OPjoI/XjHTPGgQ4dmqfr8a3p8bbbGltuvy23HaT9ttx+W247pH/7TT2vX5YpEWUSGhrK22+/Ta1ataT4eWp16ACDB8OhQ2oIn5eXxUOwt4cZM6BZM5gzBz78EMqUsXgYQgghRJb0zKTU2bNnqV+/Pl5eXkyYMIERI0YQFxfH4MGD6dy5Mx07dsTR0ZGoqCiqVq1KQkIC0dHRT91nkSJFiI2NTbLu+vXrzJo1i9mzZ5vX+fv7Ex8fT+4n9JN2dnbG2dk52XpHR0frn6T/N1OM7s03ccyTx2KHNbV99mwIDFTnbh99ZI+j4/PV/MqMMsTP3Ypsuf223HaQ9tty+2257ZB+7X+ZfS5duhRnZ2eyZctGQkICu3btws3NjaioKCpUqEDXrl2ZOnVqGkabxRUqpGYw/usv2LABhgyxShhNm8Kbb8Jvv8Fnn8F/IzOFEEII8ZKeWei8XLly3LlzBxcXF86ePUvDhg0JCgqiUaNGzJw5k+nTpxMeHk6FChUIDw9/ZkIK4M033+TcuXPMnz+fwMBAvv/+e06ePMlbb71FRESEeTaaSZMm0bRp0+cuom51RiOsXq1eW2HWvYgImDRJvR47FlxcLB6CEEIIYZN++eUXfvrpJ+bNm0dsbCxfffUVffv2JS4uDnt7e1q0aGHTicQXYuVZ+Ey+/Vb1mtq8Gf7806qhCCGEEFnGM5NSOp2OnDlz4uLiwtq1a5k9eza///475cqVo0mTJhw6dOi5D5onTx62bdvG0qVLKV26NN999x3r1q3Dy8uLhQsX8tFHH+Hp6cmmTZv45ptvXqhhVnXwoOpi7uEBrVpZ/PDTp0NYmOpa/t/EP0IIIYSwgF9//ZU//viDHTt24OHhwf79+zl79iy5c+dm4sSJ9O/fnzFjxlg7zMylQwf1fPCg6gZuJa++qobuAQwdCgaD1UIRQgghsoxnJqVMTLPrlSlThiVLllClShWqVq3KDz/8ACSvm/As9erV49ChQ8TExHD16lVat24NQJs2bbh69SpLly7l/PnzlCtX7rn2myGYekm1b2/xbkohIaruAajZYhyeu5S9EEIIIV5GUFAQkPTcyM7Ojl69enHo0CE2bNjAJFOXZvFshQurIXyghvBZ0bhxkDMnnDwJS5ZYNRQhhBAiS0h1UurBgwdERkbSokULTp48iaenJ1OmTOHKlSvAw6RVWihQoABvvvkmeSxYiynN6PWwbp16bYWhe998Y0dUFFSr9vDGohBCCCEsp0mTJjRu3Bij0ZjsvUKFCrF582amTJnC4cOHrRBdJpVBhvDlyQOmjm6jR0NkpFXDEUIIITK9VCelXF1d0el0tG3blrFjx5IrVy4++eQTBg8eTEJCAnFxcekZZ+axezeEhkK+fNC4sUUPHRKSjfnz1Y900iR4zs5rQgghhEgDx44do3fv3nh6etKsWTNu3bpFYmKi+f2SJUvy2Wef8emnn1oxykzGdKft77/h1i2rhvLRR1CyJAQFQWasMiGEEEJkJKlOSvn5+eHm5sb48ePN6/r3789vv/0GQJcuXdI+uszINHSvUyeLj51bs6YMCQk6GjZU0xYLIYQQwvJcXFzo0aMHZ86coWrVquzevZuoqKgk2/Tv35+goCDu3btnpSgzmSJFoG5d9drKQ/icnMA0geL06XDjhlXDEUIIITK1VCelUuLk5ISzszNOTk6MHTs2rWLKvGJjYeNG9drCQ/cuXIA9e4oCMHmy9JISQgghrM3BwYFvvvmGd999lwsXLiR5z9PTkz///JPcuXNbKbpMKIMM4QNo1w58fCAuDj7/HNKwioUQQghhU567K0+/fv24evUqDk/oBVS2bFm+++67lw4sU/rtN4iKgmLFoE4dix563Dh7jEYdvr5Gatd+qVyjEEIIIdLIvHnzsLOzQ6fTYTAYcHJyom/fvnz//fds2LCB/fv3WzvEzKNjR/jkEzWE7/ZtKFTIaqHodGpimerVYc0aNePxuHFWC0cIIYTItFKdvTh9+jSghvGNHTuWUaNGcfPmTb744gtCQkL44osvGDlyJH5+fty+fTvdAs7QTEP3unSxaFelY8dg40Y7dDqNr76S+YmFEEIIa8mePTt58uTBw8OD06dPM2rUKA4fPsyhQ4f4559/uHjxIkFBQXz11VfMmjXL2uFmLkWKqJt+mmb1IXwAVas+nPF4/Hj46ivrxiOEEEJkRqnuKVW9enU+/vhjYmJi8PHxAcDd3R0fHx9y5cplXlepUqXMOWvey3rwQPWUAosP3Rs1Sj03aBBIhQoFLHpsIYQQQjz0yiuvcPr0abp27YrRaESn07F48eIk2/Tt25c+ffpQtWpVK0WZiXXqBIcOwc8/w6BB1o6GIUMgMRE+/RTGjgU7OzUrnxBCCCFSJ9VJqVdffRUPDw+uXbtG0aJF0TSNkJAQihYtan4G6NatG1OmTEm3gDOsX36B+HgoVw4qVrTYYffuhT/+AAcHja5dLwCSlBJCCCGsRfdfT2ndIz2mo6Oj6du3L1WqVKFy5cq8//77lCxZ0lohZm4dO8LQoXDgANy5AwULWjsihg8Ho1HVlhozBuztYeRIa0clhBBCZA7PHL63Y8cOpk+fjrOzM2PGjKFChQpcuXKFy5cvU7FiRS5fvkzNmjW5fPkyly9fZuLEiZaIO+MxDd3r2tViQ/c07eFJz3vvGSlQIMYixxVCCCHE02mPVL5OSEigdOnShISE8O2339KxY0fOnz9vxegyMS8vqF1bnQSZJpfJAD77DCZNUq9HjYJvvrFuPEIIIURm8cyeUh4eHixfvpw7d+4QEBAAwMKFCwEICQlh0aJF3Llzh23bttG8eXOcnZ3TNeAM6e5d2LVLve7a1WKH3bIFDh+GbNlg5EgjJ05Y7NBCCCGESEF0dDR//vknwcHB5nW5cuXiq0cKDm3bto22bdty5MgR6TH1Ijp2VCdA69fDwIHWjsZs5EjVY2r0aBgxQvWYGj7c2lEJIYQQGdsze0rVrVuXY8eOMWDAAHx8fKhXrx5RUVFERUXRr18/bt26RaNGjRgzZgzFixdnzZo1log7Y1m/Xp2F1KwJJUpY5JAGA3zxhXo9eHCG6L0uhBBC2Lz8+fMzceJE4uLicHV1RdM0GjdubH60adOGVq1aMWDAAEbKGK8X07Gjet6/H4KCrBvLY774QhU9B1VnylQIXQghhBApS1VNqQcPHuDu7s7UqVPx8PCgYMGC3Lp1C0dHR/M2vr6+BAYG8vPPP9OlS5d0CzhDenTongUPeeYM5MypuowLIYQQwvoOHjyYZHnVqlXodDp0Oh1Go9E8rG/AgAEUK1aM8PBwcuXKZY1QM69ixdSNwH/+UUP4BgywdkRJfPmlulc5fjwMG6aKnw8ZYu2ohBBCiIwpVUmpmJgYVq1axdGjRwH48MMPOX36NN7e3uZtfvvtN7744gt+/vnn9Ik0owoIgIMHVR2pt9+2yCETEtQJD6iEVK5coNdb5NBCCCGEeAaj0Ui2bNmIj4/n9ddf5/XXX+eff/5JcjOvYMGC7Nq1SxJSL6pTJ5WUWr8+wyWlQM3EZzTC11/DJ5+oxNTHH1s7KiGEECLjSVVSSqfTcfnyZb744gs6dOiATqfj/fff59133zVvU6lSJYYNG5ZugWZYpuGKjRpZbAzdwoXg7w/588sJjhBCCJGRDB06FBcXFwBGjRoFQHh4OL169aJYsWLm7VxcXOjevbtVYswSOnZU4+P271e1PfPnt3ZESeh0qqeUwaAKoA8erGpMZaASWEIIIUSG8MyaUiaenp5kz56dzz//nEWLFjF+/Hhq1qxpfly5coWaNWvaXmJq1Sr1bKGhe9HR6q4bqGmHs2e3yGGFEEIIkQouLi44Oztjb2+Ps7Mzzs7ONGzYkJ07d5qXnZ2dOXLkiNSUehnFi0ONGqo7Ugaahe9ROh1MmKCKngN89BHMm2fdmIQQQoiMJlU9peLj43Fzc2PUqFGMGjUKf39/Ro4cye+//868efOoU6cOAAaDIUnX9Czv7Fk4fRocHaFDB4sccvZsVdPT2xv69bPIIYUQQgiRSpMmTQJg6tSpjB07FoDIyEhWr15N586dKVu2LADbt29n8eLFVoszS+jUCf79Vw3h+/BDa0eTIp1O9ZQyGGDaNDXS0M4O+ve3dmRCCCFExpCqpNQrr7zCyZMnzcve3t6sWbOGZcuW0bx5c3Lnzp1uAWZopgLnb7yhCjuls/Bw+OYb9fqrr8DJKd0PKYQQQojnpGlakoSTu7s7y5Ytw8PDw7zujTfe4I033rBGeFlHx46quOa+fRAcDPnyWTuiFOl06vzNaITp0+GDD1RiSm4uCiGEEM8xfC8l7777bpKE1LVr1146oExD0yw+6960aXD/PlSoYNGJ/oQQQgjxHHQ6HZ07d06y7u2336Zw4cJWiiiL8vaG6tUz9BA+E51Oncd98olafv99+Okn68YkhBBCZASpTkoZjUbzHb3ExETWr18PwLp16wDQ6/WUKlWKxMTEdAgzA/rnH7h2TRV1at063Q935w589516PXGiKpYphBBCCGHTOnVSz/+dl2ZkOp3qKTV4sFp+7z2QEZxCCCFsXaqTUnZ2dhw7dgxQ3dKHDh0KwBdffAGAg4MDmqbh4JCqEYGZn6mXVNu2Fqk2PmECxMRAnToWyYEJIYQQQmR8pqTU3r1qCF8Gp9PBzJmq6LmmQd++sHSptaMSQgghrCdVSanIyEgCAgLI/l/yxdHREfv/uuqYpj3W6XTodLp0CjODMRhg7Vr12gLj6K5dgwUL1OtJk9QJjRBCCCGEzfP2hmrV1BC+X36xdjSpotPB99+roueaBr17w4oV1o5KCCGEsI5nJqWCgoKoU6cOCxcuJCwsjEqVKlGpUiWCgoKoVKkS165do1KlSlSsWNES8WYMe/eqKfBy54bmzdP9cGPHQmKiOlTDhul+OCGEEEJkMKGhoXh7exMQEJCq7fft20fZsmXx9PRkxowZ6RuctWWiIXwmOp2aUfmDD1RiqmdPWLXK2lEJIYQQlvfMpJROp6NHjx5MmDABd3d3Zs+ezffff0/u3LmZPXs2hQoVYvbs2cyePdsS8WYMpqF7HTum+xR4p0/DypXq9X+zTAshhBDChoSGhuLr65vqhFRISAht2rSha9euHDp0iJUrV7Jnz570DdKaTEmpPXsgJMS6sTwHOzuYM0cVPTcaoUcPWLPG2lEJIYQQlvXMpFT+/PnNM8g4Ozvj4+NDw4YNcXFxwcfHBzc3N/M6mxAfDz//rF5bYOje6NHqDlrHjqp3uhBCCCFsS5cuXejWrVuqt1+5ciWFChVizJgxlCpVii+//JJFixalY4RW9sorULVqphrCZ2JnB/PmqaLnRiO88w78N4eQEEIIYROeWZX80qVLlCtXjjfeeIPIyEgWL16MpmlERUXx008/ce/ePfM6TdNYvHgxvXv3tkTs1rFjBzx4AIULw+uvp+uhDh2CzZvVCcvXX6froYQQQgiRQf344494e3sz2DRt2zOcPHmSRo0amWt91qxZkxEjRjxx+/j4eOLj483LERERgJpZWa/Xv0TklmP31lvYHz+Ocd06DC94HmpqqzXa/L//gV5vz9KldnTrpmE0GujQQbNoDNZsf0Zgy+235baDtN+W22/LbYf0b39q9/vMpFTp0qU5fvw4P//8M0ePHqVv377kyZOH+vXrs2XLFqpWrcrmzZsB8PX1Zc2aNVk7KWUauvf22/Bfsff0oGkwapR63asXvPpquh1KCCGEEBmYt7f3c20fERFBuXLlzMseHh7cvn37idtPnjyZ8ePHJ1v/xx9/4Orq+lzHthbXPHloBrBnD7vWrCHBw+OF97Vz5840i+t5tG0LN268xp49RXnnHTtOnDhK3bp3LB6HtdqfUdhy+2257SDtt+X223LbIf3aHxMTk6rtnpmUAszFzceMGcOCBQsYN24c+fPnZ/78+S8VZKYTFaW6LkG6D93buVPVU3dyUoXOhRBCCCFSw8HBAWdnZ/Oyi4vLU08MR44cydChQ83LEREReHl50bx5czxeIrljadoPP2Dn50ezmBi0Ll2e+/N6vZ6dO3fSrFkzHB0d0yHCZ3vjDXjvPSMrV9oxY0YNqlc30K6dZXpMZYT2W5Mtt9+W2w7Sfltuvy23HdK//aae18+SqqSUyfLly/H29ubMmTPcv3+fdevWUbJkSapWrfpCQWY6mzZBbCyUKpWuBZ4e7SU1YAAULZpuhxJCCCFEFpM7d25CHin4HRkZidNTJmZxdnZOksQycXR0zFwn6Z07g58fDhs3Qv/+L7wba7bb0RGWLlWvV67U0a2bAz//rHpRWS6GTPZzT2O23H5bbjtI+225/bbcdki/9qd2n88sdG4yY8YMhg4dil6vJ3/+/JQpU4a7d+/y9ttvU6NGDZYsWZKkHkGWZBq617Wrmss3nWzYAMeOgZvbw+SUEEIIIURq1KhRg0OHDpmXT5w4QeHCha0YkYV07Kied++GsDDrxvIS7O1hyRJ1upmYqCYX3LLF2lEJIYQQ6SNVSamjR48yffp09u/fT9tHbtUMGjSIS5cu8fnnnzNx4kRq1aqVboFanaZByZLg6ZmuQ/cSE9WMewBDh0LevOl2KCGEEEJkYhERESkWEW3Tpg1///03u3btQq/XM3XqVFq0aGGFCC2sVCmoXBkMBvj1V2tH81IcHGDZMlXCVK9X+bY5c9QMfUIIIURWkqqkVPXq1Tl//jyVKlVK9p5Op6Njx46cPXuWVatWpXmAGYZOB7NmwZ076Vp1fOdOuHgR8uSBYcPS7TBCCCGEyOQqVarEb7/9lmy9p6cnM2fOpFWrVuTPn5+LFy8y2nTHK6vr1Ek9r19v3TjSgIMDrFihRiUmJMBHH0G9enD6tLUjE0IIIdJOqofvPavQpZOTU5KZXrIsh+cqw/Xc9u1Tz+3aQSaqLSqEEEKIdKZpGsWLFzcvBwQE0K5duxS3/eCDD7h48SIrV67k1KlT5M+f3zJBWpspKbV7N9y7Z91Y0oCDA6xaBf/7H7i7w+HDULUqfPGFKnMqhBBC/L+9+46vurr/OP662QkkZEBYCSQsEcsWwYGogMh0/aoMW7FiFRUUlVasVrEKauuuowU1qICjKlZREVBUCg4gzAARDJAww0xCIGR8f38cb242AXJXvu/n43Eed+Tey/nkJl9O3vec8/V3tQ6lxDO+/dZcXnyxd/shIiIi/i05OZnBgwfTsGFDb3fFczp0gC5dzH4Ifr6EzykwEO64A9LS4OqrTWnTppkyFy/2du9ERETOjEIpH5KfDz/9ZK4rlBIRERE5DfVoCV9ZCQnw4YemtWgBW7bAgAFw442wf7+3eyciInJ6FEr5kO+/N59+JSZC69be7o2IiIiIH3KGUosW1YslfBVdfTVs3Gj2mHI4zIboHTvCW2+Z8/KIiIj4E4VSPqTs0j2Hw7t9EREREfFLZ50FnTubT/rq6Ul4oqLgxRdh2TL4zW/gwAH4/e9h4EAzg0pERMRfKJTyIdpPSkRERKQO/O535nLSJLPerZ7q0wdWrYLp0yEszOwx1bmzuV1Y6O3eiYiInJxCKR9x4gQsX26uK5QSEREROQOTJsHIkWa21HXXwXvvebtHbhMcDPffD+vXmz2mjh+HBx6Anj3N1hAiIiK+TKGUj1ixwgwimjQxs85FRERE5DQFBZlNlm64AYqLYdSoeruUz6ltW/jyS7PHVFwcrFsHF1xg9p7KyfF270RERKqmUMpHaD8pERERkToUFAQpKTB2LJSUmCV9b77p7V65lcNhyty0yZyVz7LgpZegUyeYN8/bvRMREalMoZSP0H5SIiIiInUsMBBeew1uucUEU2PHwuuve7tXbte4scnjFi2Cdu1g505z1r6rr4asLG/3TkRExEWhlA8oLoalS811hVIiIiIidSggAF59FW6/3Uwduvlm+Ne/vN0rj+jfH9auNXtMBQWZ2VKdOpnZU8XF3u6diIiIQimfsGYN5OZCo0bmjCkiIiIiUocCAuCf/4S77jK3b7vN3LaB8HB4/HFzlr4+fcyY88474cILTWAlIiLiTQqlfIBz6d5FF5lZ5iIiIiJSxxwOePZZuO8+c3vCBHPbJjp3hv/9z8ySioyEH34wZ+ibMgWOHfN270RExK4USvkA7SclIiIi4gEOBzz1lEliAO65x9y2iYAAs4px40a45hooKoInnjCB1eLFOtOOiIh4nkIpL7MshVIiIiIiHuNwmPVsDz9sbv/5z+a2jbRsCR98YPaYatkStm6FwYODePLJXvzwg8IpERHxHIVSXrZxIxw4ABER0KOHt3sjIiIiYgMOBzzyCPztb+b2gw8S8Oij5tNCG7nySkhLMysZHQ6L5ctb0LdvEL17w9y5UFjo7R6KiEh9p1DKy5yzpM4/H0JCvNsXEREREVt58EGzfg0IfOwxzp4923bBVFQUvPAC/PRTEZddtoOQEIsff4TRoyEpyUwi27/f270UEZH6SqGUl2npnoiIiIgX/fnP8PTTAHT4z38ImDLFdsEUQJcuMHFiKlu3FjF1KjRrBrt2mdwuMRHGjYN167zdSxERqW8USnmR9pMSERER8QH33EPxr2fiC3zmGbMBug2DKYCmTeGvf4Xt2+Gtt8wZ+o4fh9deM8FV//7wySdQUuLtnoqISH2gUMqLMjJg504IDobevb3dGxERERH7KrnjDtbcdpu58dxzZqMlGycvISFwww3w00+wdCn89rfm7H1ffQUjRkCHDvD885CT4+2eioiIP1Mo5UXOWVLnnQfh4d7ti4iIiIjdbbviCopefdVshP7SSzB+vK2DKTDfigsvhPfeMx+o/ulPEB1tzth3992QkGAut271ckdFRMQvKZTyIi3dExEREfEt1h/+AG+8YdKYf/8bbrkFiou93S2f0KoVPPkkZGXBK69Ax46Qm2tmTLVvb87m99VXtl35KCIip0GhlBcplBIRERHxQTfeCG+/bdarvf463HSTgqkyGjSA226DDRvgiy9g8GATRP33v2bPqa5dzR5Ux455u6ciIuLrFEp5yc6dZppzQABccIG3eyMiIiIi5YweDXPnQmCg2fH7d7+DoiJv98qnBATAoEHw2WewcSPcfjtERJiz9I0bZ87a9+CD5ix+IiIiVfFaKDVx4kQcDkdpa9euHQDr16+nV69exMTEMHnyZKx6Ov/3u+/MZffuEBXl3b6IiIiISBWuuw7efReCgkxANWoUFBZ6u1c+qWNHsw1XVhb8/e/QujUcOACPP26ujx4NP/7o7V6KiIiv8VootWLFCubPn8+hQ4c4dOgQqampFBQUMHz4cHr27MmKFStIS0sjJSXFW110Ky3dExEREfED114LH3xgTpf8n//A9dfDiRPe7pXPiomB++6DLVvMt61vXzPBbO5cc7bp88+HlBTIz/d2T0VExBd4JZQqKipiw4YNXHzxxURHRxMdHU1kZCSff/45R44c4ZlnnqFt27ZMmzaN1157zRtddDuFUiIiIiJ+YsQImDcPQkPho4/g//4PCgq83SufFhQE11xjxrwrV8Lvfw8hIfD992aLrubNzXK/Vau83VMREfEmr4RS69ato6SkhG7duhEeHs4VV1zBjh07WLNmDX369CEiIgKALl26kJaW5o0uutX+/WZjSICLLvJuX0RERESkFoYMMTt5h4XBJ5/A1VfD8ePe7pVf6NEDZs2C7dvNcr42bSAnx5zBr2dP8/VXXoEjR7zdUxER8bQgb/yjaWlpnHXWWbz44os0btyYSZMm8cc//pFzzjmH5OTk0sc5HA4CAwM5dOgQMTExlV6noKCAgjKfUuXk5ABQWFhIoQ+v91+yxAEE0amTRaNGRXWyNYGzXl+u213sXDvYu3471w6q387127l2cH/9dv2+Si1cfjl8+ikMHw6ff+6aQfXrB6pSs2bN4IEH4P77YckSmDEDPvwQUlPNrKl774Xf/hZuuQUuvBAcDm/3WERE3M0rodSYMWMYM2ZM6e2XX36Z5ORkzj77bEJDQ8s9NiwsjPz8/CpDqenTpzN16tRK93/55Zels6180VtvnQO0o1WrbXz22do6fe2FCxfW6ev5EzvXDvau3861g+q3c/12rh3cV3++NruRmvTvbwKpoUNh4UKzw3ebNhAf72pNmlS+Hh1tTlcnBATAZZeZduCAObnhjBmQlgZvvmlax47mDH6//735FoqISP3klVCqovj4eEpKSmjWrBnr168v97Xc3FxCQkKqfN6UKVO45557Sm/n5OSQmJjI5ZdfTpQPn9Lu0UcDARg9OpEhQxLq5DULCwtZuHAhAwcOJDg4uE5e01/YuXawd/12rh1Uv53rt3Pt4P76nTOvRarVrx8sWGCW9GVmmnYyQUEmXakqsKrqdsOGtpgqFBcHd98Nd91l9puaORPeeQc2bTIbpk+ZAlddZQKqAQOU64mI1DdeCaUmT55M9+7dGT16NADLly8nICCAzp07M2PGjNLHZWRkUFBQQGxsbJWvExoaWmlmFUBwcLDPDtJzcmD1anP90kuDqOtu+nLt7mbn2sHe9du5dlD9dq7fzrWD++q38/dUTsGFF0JGhhnY7dsH2dnm0tnK3j5yxJyCbvdu02ojLMwVUiUmwuTJcMEFbi3JmxwOc2a+88+HZ581wdTMmfDTT/D++6a1bg0332w2Sk+om891RUTEy7wSSnXt2pUHH3yQpk2bUlxczIQJE/j973/P5ZdfTk5ODm+88QY33XQT06ZNY8CAAQQGBnqjm26xbBmUlJhZ3vrPVERERMSPxcaaNWgnU1BgznRTXWhV9vbevXDsmNlE3TkLa+VK+PhjmDQJHnsMwsPdX5sXRUXBH/9o2po1Jpx6+22zUfpf/wqPPAJXXGH2nho6lDr/kFdERDzHK6HUDTfcwIYNG7j22msJDAzkhhtuYNq0aQQFBTFz5kxGjRrF5MmTCQgIYMmSJd7oott8+625vPhi7/ZDRERERDwkNBRatjStNo4eLR9avf++2WjpmWfMRuspKWZKkQ107QovvghPPWU2RZ8xA775Bj77zLRmzWDsWDODql07b/dWREROlddWZU+fPp3Dhw9z4MABnn/+eRo0aADAiBEj2Lp1K7NmzWLjxo106tTJW110C4VSIiIiIlKjBg0gKQnOOw+GDYNZs0wY1bw5pKfDRReZ5XzHjnm7px4THg5jxpiz9m3eDH/6k1nZuGcPPPEEtG9vJq3NmWMmmYmIiH/wya0CmzVrxtChQ4mLi/N2V+rUsWPw44/mukIpEREREam1oUNhwwZzOrqSEvjHP6B7d7M7uM106ABPPglZWfDBBzB4sNmT6uuvTXDVooVZ+vfxx2bSmYiI+C6fDKXqqx9+gMJC8x9lmzbe7o2IiIiI+JWYGDNr6r//NbOmNm82G67bbNaUU3AwXHONWca3bZvZa6pVKzh0yCzzu+oqs+3XFVeYJYC//OLlDouISCUKpTyo7NI9G5zhV0RERETcYfhwM2vqd79zzZrq0cOWs6acWrWChx82wdPChTBhAiQnw4kTsGABTJwIbdvC2WebDG/JEvNhsYiIeJdCKQ/SflIiIiIiUidiYszm5//9r9nte9MmM2vqz3+29aZKgYEwYAC88AJs3QppafD3v0O/fuZrmzaZDO/SS6FJE7juOvNtzM72ds9FROxJoZSHnDgBy5aZ6wqlRERERKROOGdN3XCDmTX11FNm1pRzI1MbczjMzKj77jMzo/bvh3ffNdtyNWkCR46YExveeCMkJATxpz/15fHHA0hNBcvydu9FROxBoZSHrFpllvrHxZn/HEVERERE6kRsLLz1ltnZu1kz2LgRzj8f7r/f1rOmKoqONjOjZs2C3bvNascHHzT7xVuWg/T0WKZODaRHD0hIgFtugXnzIC/P2z0XEam/FEp5iHPpXt++EKDvuoiIiNTS+vXr6dWrFzExMUyePBnrJFM4LMti/PjxxMbGEh0dzdixYzlmw02wbWnECDNraswYM2vqySc1a6oagYHQuzf87W/mw+Nt2wq5445Uhg8voUED2LULZs6Eq682HyoPGuRaEigiInVH8YiHaD8pEREROVUFBQUMHz6cnj17smLFCtLS0khJSanxOW+99RabN28mNTWV7777jg0bNjB9+nTPdFi8LzYW3n7bTPFp2tQ1a2rKFCgo8HbvfFaLFjBw4A4++KCYAwdcm6O3aWO24fjyS7jrLmjXzrUk8Ouv9S0VETlTCqU8oLgYli411xVKiYiISG19/vnnHDlyhGeeeYa2bdsybdo0XnvttRqf8+OPP/J///d/tG7dms6dO3PVVVexZcsWD/VYfMaVV5pZU6NHm1lTTzxhZk399JO3e+bzQkPh8svh+edhyxaT6zk3Rw8KMpulP/00XHaZyQAHD4ZnnoG1a7UXlYjIqQrydgfsYN06s5FiZCR07ert3oiIiIi/WLNmDX369CEiIgKALl26kJaWVuNzzjnnHN566y2uvfZajh8/zjvvvMM999xT7eMLCgooKDPdIycnB4DCwkIKCwvroAr/4Ky1XtUcFQUpKTiuvprAO+/EkZaGdf75lNx7LyUPPWTSl1/Vy/pPQU31t21rZk1NnGjG9AsXOvj88wC+/NLB3r0OvvgCvvjCPLZpU4vLLrPo37+E/v0tWrb0ZBWnR++96i97aSd2rh3cX39tX1ehlAc4l+5deKH5dEVERESkNnJyckhOTi697XA4CAwM5NChQ8TExFT5nHHjxvHKK6/QrFkzAIYPH86NN95Y7b8xffp0pk6dWun+L7/8sjQMs5OFCxd6uwt1LziY4L//nS4zZpDw3XcEPvUUR+fOJXXiRA63b1/uofWy/lNQm/ojIuDaa+Gaa2D79kjWrm3C6tXxbNgQx969Qcyd62DuXLMgJSEhl65ds+nadR+/+c0BIiKK3F3CadN7r/rtys61g/vqz8/Pr9XjFJF4gPaTEhERkdMRFBREaJnZLABhYWHk5+dXG0o9//zzREdHs337dhwOB7feeiuTJ0/m6aefrvLxU6ZMKTeTKicnh8TERC6//HKioqLqrhgfV1hYyMKFCxk4cCDBwcHe7o57jBxJ0bx5BN55J1GZmVx8//2U3HcfJQ8+SGFAQP2vvwZ18f4XFFj88EMRixY5WLzYwcqVDrKyIsnKimT+/DYEBVmcd55F//6m9epl4Qvfalv87NdA9du3fjvXDu6v3znz+mQUSrmZZSmUEhERkdMTGxvL+vXry92Xm5tLSEhItc+ZPXs2jz76KK1atQLMTKh+/fpVG0qFhoZWCr4AgoODbTlIr/d1//a3ZjOkCRNwzJ1L4JNPEjh/vjnVHDao/yTOpP7gYOjf3zSAQ4fMZuiLFsHChbBli4NlyxwsW2bO+hcZafapGjAABg6Es84Ch6MOiznl/uu9V/32rN/OtYP76q/ta2qjczfbvBmysyEsDM4919u9EREREX/Sq1cvli9fXno7IyODgoICYmNjq31OSUkJ+/btK729Z88eiouL3dpP8TNxcTBnDnzwAcTHw/r1BF14IZ1mzYJafrItJxcTY5b4vfwy/PwzZGTAjBlw3XXmLcjNhf/+1+xVdfbZ0KoV3HSTeWv27vV270VEPEOhlJs5Z0n16VNuL0kRERGRk7r44ovJycnhjTfeAGDatGkMGDCAwMBADh8+XGXY1LdvX5544glSUlL497//ze23386IESM83XXxB9dcY87QN3IkjuJi2n/0EUFnnw2vvAJFvrv3kb9KSoJx4+Ddd2HfPli50pwUsX9/83dCVhakpMCYMdCsmTlB0n33wYcfmkBLZ/YTkfpIy/fcTEv3RERE5HQFBQUxc+ZMRo0axeTJkwkICGDJkiUAxMTEkJqaSrdu3co957HHHiMnJ4c//elP5ObmMmjQIJ5//nnPd178Q+PGMHcuRdddx7EJE4jcuRNuvx1eeAGeegqGDfPumrJ6KiAAevQw7c9/hmPHYOlSs8xv0SJITYW1a01zrryNjoZu3aB7d/O87t3Nkj+dSElE/JkOYW5kWfDNN+a6QikRERE5HSNGjGDr1q2sXLmSPn36EBcXB4BVzbSJ6Oho3nzzTU92UeoBa9gwvi4pYejOnQT+7W+waROMGGE2PfrHP0wKIm4THm72lRo40NzOzoavvoLFi82MqnXr4PBhWLLENKewMOjSxQRUzta5s3k9ERF/oFDKjbZvN9Nwg4LM8j0RERGR09GsWTOGDh3q7W5IPWcFBVEyfjyBN95o1pU9+6zZqbtnT/jd7+DxxyEx0dvdtIUmTeD6600DOHEC0tLMDCpnW70a8vLgxx9NcwoMhI4dXbOpunc3M6yio71QiIjISSiUciPn0r1zz4UGDbzbFxERERGRWmnUCKZPh9tugwcfhLffhrfegvffh0mT4P77ISrK2720lZAQEyx162Y2QwcoKYGtW01AtWqVK6zKzjZbhW3YYN42p+Tk8jOqevSA5s29UY2IiItCKTfSflIiIiIi4rdatzapxl13mR23v/nGhFUzZ8LUqXDLLdrQyIsCAqB9e9Ouu87cZ1mwa1f5GVWpqbBtm9ksPSPDbJzu1LQpdOsWSIMGndi/30GnTmafqhpO8CkiUqf0v4gbKZQSEREREb937rlmGd8nn8DkyZCers3QfZTDAS1bmjZsmOv+gwfNcr+yQdWmTbB3LyxYEAC0LxdWNW5swqmzzjJLAZ3X27SB4GBPVyUi9ZlCKTfZvRt+/tn8x3Dhhd7ujYiIiIjIGXA4zMbngwfDv/8NjzyizdD9SGwsXHaZaU75+WYD9RUripk/fwcFBa1JTw8gKwv27zftf/8r/zpBQSaYcoZUZYOrxo2VTYrIqVMo5SbffWcuu3bVpoIiIiIiUk8EB8Mdd8ANN2gzdD8XEQG9e0OPHiUkJKxlyJAEgoMDOHrUTIbbvNm0TZvMZXo6pV9LTzcT58qKiakcVp11FrRrB6Gh3qlRRHyfQik30dI9EREREam3ym6G/pe/wOzZ2gy9nmjQwLUZelmWBTt3Vg6rNm+GHTvg0CH4/nvTygoIgKQkM5vKuQdWhw7mMjHRnC1QROxLoZSbKJQSERERkXqvdWtzdr6774Z77zWDYG2GXi85HJCQYFr//uW/duyY2bqkYli1eTPk5sIvv5hWUUgItG3rCqvKhlYtWphAS0TqN/0P4QYHD5r12QB9+3q3LyIiIiIibnfuubBkiTZDt6nwcOjSxbSyLAv27HGFVT//7Gq//AInTsDGjaZV9Zrt2lUOrNq3h2bN9OMkUl8olHKDpUvNZceOEB/v3b6IiIiIiHiENkOXChwOaN7ctEsuKf+14mKz7K9sUFU2sDp2zHzQ7/ywv6yGDasOq9q314brIv5GoZQbaOmeiIiIiNhW2c3Qp0+H555zbYY+ciT062fWZ2mNlq0FBkJysmmXX17+a4WFsG1b1YHV9u2QlwepqaZVFB0NZ59tJgicfbarJSVp/yoRX6RQyg0USomIiIiI7TVqZM7QN368azP0d94xzSk8vPLu187ASlNebCs42PVjUVFBAWRkVA6r0tMhMxMOH4bly00rKzTU/FiVDarOPtuEYiLiPQql6lhuLqxaZa4rlBIRERER2yu7GfrcuSY9SE93rdFau9a0iho1Kh9Ulb1s1MjjZYhvCA01s6A6dqz8NeeG65s2ufaq2rjR7GlVUFD1ckCHI4j4+AH07BlIp07lZ1nFxHimJhE7UyhVx5YvN+ujk5LMKU5FRERERASzGfq557puFxWZNVrp6a6pLs7LHTvgyBH46SfTKoqPrzyzqn17szN2eLjHShLfUt2G68XF5ketYli1cSMcPuxg794GfPYZfPZZ+ec1beqaUVV2OWDLlprEJ1JXFErVMS3dExERERGphaAgEyK1a1f5a8ePw9at5YMq5/U9e2DfPtOcZxgqKzHRpBLnnQe9e0OvXhAb6/56xGcFBkLbtqYNHeq637IgK6uQWbN+IDr6fNLTA0vDqp07Ye9e05YsKf96DRuaZX+tWpmJgK1alb/evLn2rxKpLYVSdUyhlIiIiIjIGQoLg3POMa2inBzYsqV8UOW8fviw2VgoMxPmz3c9p107E1Cdd55p3bqZf0NszeGAZs2gc+cDDBlSQnCwK0nKza16ZtXWrWaj9erODAgmb01IKB9Ulb1MTIQGDTxUpIiPUyhVh44fhx9+MNcVSomIiIiIuEFUFPToYVpZlgUHDpgNhFatMgPzH380odWWLabNnm0eGxwMXbu6QqoePaCkxPO1iM+KjDST7Hr1Kn//iRMmmNq+3awyrXiZleVambptW/WvHxdXObAqez0+XksExR4UStWhH380B6lmzaqehSwiIiIiIm7icJgz9jVuDBdeCBMmmPsPHjT7Uv34o2k//ADZ2bBihWkvv0wwMCQigsA+faBPH1dY1by5V0sS3xMS4tpbqirFxbBrV9WBlfN6bq7JTw8ccJ0kq6LQULNNmjN/7dnTTPBr2NBtpYl4hUKpOlR26Z5SbRERERERHxAbC4MGmQZmRtX27a6A6scfsVauJDg/H776yjSnxERXQNW7t0kGlApIDQIDzY9NYqLJRiuyLLOHf3WB1Y4dsHu3OVvg+vWmvfmmea7DYTZc79nTtB49oHt3M6tLxF8plKpD2k9KRERERMTHORzmVNlJSXDddQAUHTvG0ldfpW9YGEErV5rAasMG1/5UH3xgnhsQAJ06ufalio01ywmjoqBRI9f1qCgzpUakAocDoqNN69q16secOGF+7NLSYOVKV9u927W31dtvu16vQ4fyQVWPHuZHUMQfKJSqI4WFsGyZua5QSkRERETEjwQFkdOmDdaQITB+vLkvN9esrSozo4rMTNf0lZMJCysfUlUMrcrerulrwcHurV18TkiI62yBw4e77t+92/xIOkOqVavMHlabN5s2Z47rse3bVw6qoqM9XorISSmUqiOpqXD0KMTEVH2SEBERERER8SORkdCvn2lOu3e79qbauNGcCbBsO3IE8vPNY48fN23fvjPrR48eMHo0jBwJLVue2WuJX2veHIYONc1p715XUOW83LHD7O//88/wzjuux7ZtWzmo0tI/8TaFUnXEuXSvb18zq1dEREREROqZ5s3hyitNq05RkZll5QypqgquarpdMdxatcq0yZPhkktMQHXttebTcLG9pk1h8GDTnLKzXT82zllV27aZswZu3Qrvved6bFJSEDExffjiiwA6dHDN0GrTBsLDPV6O2JBCqTqi/aRERERERISgIBMYnWloVFRkZll9/LFZl7V0KXz9tWl33AFDhpiAatgwpQdSTpMm5ff2B3Omv9TU8ntU/fILbNvmYNu2pqSmVn6dli1dIVW7dq7rbdsqE5W6o1CqDpSUwHffmesKpURERERE5IwFBUGLFmaPq/HjzVSXd96B2bPNnlbz5pkWGQnXXANjxsCll5rn2VV2ttnst0ULb/fE58TFwYABpjkdOgSrVhXx0UfraNCgCxkZgaWzqY4cgZ07TXNOwCgrNrZ8SFU2tGreXGejl9qz8RGr7qxfD4cPQ4MG5pScIiIiIiIidSopCe6/37R168zsqTlzzAZCs2aZ1rSp2Xtq9Gjo1av+JwOWBWvWwKefwiefmL2+wKxlmzTJJDD1/XtwBmJi4OKLLfLydjBkyG8IDg4EzLf1wAHXcr+tW2HLFtf1PXvg4EHTfvqp8uuGh5cPrNq2hWbNzN7/YWEQGuq6XrGFhto7V7Ujvd11wDlL6oIL9AskIiIiIiJu1rkzTJ8Ojz9uTgE+Z47ZKGjvXnj+edPatTPh1OjRcNZZ3u5x3Tl2DBYvNkHUp5+aqTxlORzw+eem/eY3cPfdZhZZWJhXuuuPHA5o3Ni03r0rfz0vzyz9qxhWbd0K27ebt6i2J6msSmBgzaFVdV+LiDCT5BITXa1pU/N64rsUodQB7SclIiIiIiIeFxAAF11k2nPPwcKFZnnfxx+btODRR03r2dOEU9df759n8MvKgvnzTQi1eLFJPZwiImDgQLO31pAhZoP4F16A1183qci4cTBlilkCefvtJqWQM9KwIXTpYlpFJ06YYKpiYHXwoOuElAUFruvOVlTkeo3iYnNm+6NHz7yvQUHmR75sUOVszZvDkSMhWNaZ/zty+hRKnSHLUiglIiIiIiJeFhICQ4ealpcH//2vCagWLHDtbH3ffWbfKecZ/KKjvd3rqpWUwIoVrmV5q1eX/3qrViaEGj7cnJGw4iyoF14wYdzMmfDii2aJ46OPwhNPmNonTao6UZEzFhIC7dubdiqKi6sOq6oLsaq6PzfXTJzLzDRt1y4Tdm3fblplwcBgbr3VIiGh6uDK2Ro10kpQd1EodYa2bDFrakNC4LzzvN0bERERERGxvYYNXUv3srPh/ffNEr///Q+++sq022+HoUNxXHcdAb6wvik318z0+vRTMytq3z7X1xwOOP98E0QNG2aW5Z0sIYiONiHc3XfDhx/Cs8/C999DSopp/fu79p0SrwsMNJPeIiLq7jWLiszf6s6QqnKz2LPHwfHjDrZsMX/bV6dhQ1dA1bKl2SOrqhYZqfDqVCmUOkPOWVK9e2uZsoiIiIiI+JgmTUwAdfvt5gx+c+eaGVQbNsBHHxH00UcMDQjA0bSp6y/rstfLtqZN63bKSEaGa2+oJUvM2i+nqCgYNMiEUIMHmzpOR1AQXHedad9/b8KpDz4wywAXLyaoQweSLr0U+vXz3ZljclqCgiAhwbTzz6/89cLCIj7++HO6dBnMnj3B1YZXBw6YyYcbN5pWk/Dw6gOrir9KoaHuqdvfKJQ6Q1q6JyIiIiIifiEpyeyvNGUKrF0Lc+ZgzZlDQGYm7N5t2smEhlb9F3ZV91Wc9lJUZIKhTz4xQVRaWvmvt2tnluQNG2b2yQoJqbPSAejTB9591yzne/FFmDEDR3o6XdPTsd57D269Fe680z/33ZLTEhxskZwMHTpU/5j8fLOtmTOk2r3bzMCq2HJyzHZnGRmmnUxMTM2hVXy8yWKbNKnfAZZCqTOkUEpERERERPzOrztVFz36KF/Nns1l55xD8P795gx+Ff/adt535IjZyKf6TXrKi4x0/ZUdFQXLl5sdr50CA6FvX9eyPE+dJbBVK/j73+Gvf6X4tdc49uSTNNyzx+w59Y9/mFlVkybBued6pj/i0yIiTGhVU3AFJryq6tenqhCrsBAOHTLtZLOvwPwqlQ2pmjSp+bY/reJSKHUGduwwM2ADA6ueDigiIiIiIuLTHA6Ox8ZC9+4QHFzzY48dc/3VXVN4tXu3a+fp3Fz4+WfXa8TGmrPkDRtmlud5c8lcZCQld9zB4latGGpZBL34Inzzjdl/a84cM1tr0iS48krzR59IDSIiIDnZtJpYlgmjqpptVbZlZ5tWXOz6Vdq6tXZ9adjw5CFWbCxkZ3s/vVIodQa++85c9uhhkksREREREZF6KzzcLAFMSqr5cZZl/oIuG1Tt3w+dO5sldEE+9mdoYCDWkCHmjISrVsFzz8E778DSpaYlJ8PEifCHP5gZXyJnwOEwgVBsLHTqVPNjS0rg8GFXQJWdbc4BUNPtoiKzB1ZeHvzyS02vHkxc3MXceGMdFncafOxo4F+0dE9ERERERKQCh8OEN1FRJ1/z5Gt69IA33zRL+V5+GV591WwQNGkSPPww3HyzCahOFsyJ1IGAAFeAVZvVrZZ1KiGWRcOGxwDvzrBRKHUGFEqJiIiIiIjUQy1awGOPwQMPwNtvm7P2bdpkLp97zrUbtXOTd+f1ivfFxZlkQcQDHA6zgXpMzMnz4MLCIj777DtgiEf6Vh2FUqdp3z5zTAKz1FhERERERETqmYgI+OMfYdw4WLDAhFILF9b+bIWBgWYDn5oCLOf9cXHau0psxydCqSuuuIKRI0cyduxYvvnmG2677Tays7N54IEHuOeee7zdvSo595Pq3NlMpRMREREREZF6KiAABg82bd8+yMoqv+l72ea878ABs0u1c+fq2vwbZQOspCSzAX2PHuZsieHhbi9TxNO8HkrNnj2bBQsWMHLkSLKzsxkxYgT33nsvo0aNYuTIkXTv3p1LL73U292sREv3REREREREbCg+3rSTKSw0m/dUDKuqC7BKSlz3VxQYCB07moDKGVR16waNGtV5eSKe5NVQ6uDBg9x7772c9euOXbNnz6ZFixY89NBDOBwO/vrXv/Laa68plBIRERHbWr9+PTfddBNbtmxh3LhxPPXUUzgcjpM+r6SkhIsuuohrr72We++91wM9FRGRcoKDzd5ULVqc/LFFRZUDrPR0czbAVavM7KwNG0x76y3X89q2LR9Ude9eu8BMxEd4NZS69957ufrqqzl27BgAa9as4dJLLy0daJ133nncf//93uxilQ4fhjVrzPW+fb3aFREREanHCgoKGD58OIMGDeKdd95h4sSJpKSkcNNNN530ua+++ipHjhxh4sSJHuipiIickaAgaN7ctIosy+xftWoVpKa6Lrdvh61bTXv/fdfjW7asHFQlJppdsEV8jNdCqa+//prFixezYcMGJkyYAEBOTg6dOnUqfUxUVBS7du2q9jUKCgooKCgovZ2TkwNAYWEhhYWFbuo5fPONA8sKol07i8aNi3DjP1VrznrdWbevsnPtYO/67Vw7qH4712/n2sH99fvS9/Xzzz/nyJEjPPPMM0RERDBt2jTuuOOOk4ZSu3bt4oEHHuDDDz8kODjYQ70VERG3cDhcM66GDXPdf+AArF5dPqxKT4edO0375BPXY+PiyodUPXpAu3bV/5uWZZYfHj0K+fmuy7LXq7qvuq+HhUFCggnHyl4mJGgJos15JZQ6fvw4t956K6+88gqRkZGuzgQFERoaWno7LCyM/Pz8al9n+vTpTJ06tdL9X375JREREXXb6TJmzeoEtCcpaQeffbbabf/O6Vi4cKG3u+A1dq4d7F2/nWsH1W/n+u1cO7iv/prGHp62Zs0a+vTpUzqu6dKlC2lpaSd93t13303r1q3JzMxk2bJlXHDBBdU+1lsf8vkahb2qv+ylndi5dvDz+qOizH4yZfeUycvDsXYtjtRUHKtX40hNhbQ0HAcOwKJFpv3KatiQgLPPpu+RIwQ+8ADW8ePlwiRHcbFHyrAiI6FlS6zERHNZ9rozuIqKqvOZXn793tcBX/mQz2FZluWWHtTgL3/5C9u2bWP27NkAjB07lksuuYQffviBxo0b87e//Q2Aw4cP07JlS44ePVrl61Q1iEpMTGT//v1ERUW5rf99+wbyww8BvPZaEb/7nce/fVUqLCxk4cKFDBw40HafiNq5drB3/XauHVS/neu3c+3g/vpzcnJo3LgxR44ccet4ojbuvfdejh8/zksvvVR6X5MmTUhPTycmJqbK5yxfvpwLLriAIUOG0LNnT+bOncugQYP45z//WeXjH3nkkSo/5JszZ45bP+QTERHPCCgsJHL7dhplZBC9dSuNMjKIysgg6MSJWj2/JDCQ4tBQikNDKQoLK71eHBpKUWgoxWFhFIeEmPvCwsx9FVrg8eOEHzhA+P79hB04UHo9JC+vVn0oCgvjWOPGHIuLM5eNG3PceT0ujmNxcRQ1aKAlij4kPz+f0aNHn3Q85ZWZUnPmzCE7O5vo6GjAdPa9994DKPdJXmpqKi1btqz2dUJDQ8vNrHIKDg522yD96FFYudJcv+yyIHztbwF31u7r7Fw72Lt+O9cOqt/O9du5dnBf/b70Pa04ixxcM8mrC6VmzJhB7969+fTTT3E4HNxyyy20bt2aCRMmlJ5cpqwpU6Zwzz33lN52fsh3+eWXez2U8ySFvarfrvXbuXawb/1WURGF6emUbNjA6g0b6HrBBQRFRWGFh0ODBhAR4br89fsS+GurS4VHj8LOnTiysiAry1zu3Ilj504cmZnm+sGDBB0/TmRWFpFZWdXX1LAhtG6NlZSElZwMSUnmelISJCdDmVVaYN/33skTH/LVhldCqe+++46ioqLS2/fddx99+vRh7NixJCYmsmjRIvr168dTTz3FoEGDvNHFan3/vTkxQmIitG7t7d6IiIhIfRYbG8v69evL3Zebm0tISEi1z8nKymLIkCGlJ45JTEykSZMmbN26tcpQyhsf8vkyu9btpPrtW7+dawcb1h8cDF27UtipE3saNKDH5ZcT5I36o6NNO+ec6h+Tnw+/hlZkZla+npkJBw/iyMuDDRtwbNhQ9evExZlw6tcW0KoVTbKzCW7XjuB27aCK/wvtwNsf8nkllEpISCh3u2HDhjRu3JjGjRvz7LPPMmTIEBo2bEh0dDQpKSne6GK1vv3WXF58sWYGioiIiHv16tWLGTNmlN7OyMigoKCA2NjYap+TkJBQemZjgLy8PA4ePFjj7HMRERGfFREBHTqYVp38fBNObdsGGRmu9ssv5vLgQbMx/IEDsGIFYGZ9XQAwdaprM/kyoVW51rIlBNb1PDEBL559r6yywdNtt93GoEGD2LRpE3379qVhw4be61gVyoZSIiIiIu508cUXk5OTwxtvvMFNN93EtGnTGDBgAIGBgRw+fJjIyEgCKwySR40axahRoxgwYADt2rXjoYceomPHjnTp0sVLVYiIiLhZRAScdZZpVcnJKR9WZWRQsnUreevXE7l/P478fNdZC5curfz84GBo1coVUrVpU/4yLk6zVk6TT4RSFSUnJ5OcnOztblRSUGCW74FCKREREXG/oKAgZs6cyahRo5g8eTIBAQEsWbIEgJiYGFJTU+nWrVu55wwcOJAnn3yS8ePHk5mZSbdu3fjPf/5TupxPRETEdqKioGtX035VXFjI1599xpDBgwk+fLhSaFXaduyAwkLYutW0qkRGVh1WtWkDSUkQHu6RMv2RT4ZSvmrFCjh+HJo0qT6AFREREalLI0aMYOvWraxcuZI+ffoQFxcHQE0nUL755pu5+eabPdVFERER/+VwQHy8ab17V/56cbGZQbVtm2s5YNmlgbt2QW4urF1rWlWaNy8fVpW97umlgZZlNsrOyyOolmc/dCeFUqdA+0mJiIiINzRr1oyhQ4d6uxsiIiL2Exholu61alX1kqljx2D7dldIVfbyl19MYLV7t2nLllV+fnCwOYta2dlVLVqY4KigwNWOHy9/u7r7avNYyyIYuDghAa67zu3fwpoolDoF2k9KREREREREREqFh0PHjqZVZFlmk/WKYZXzcvt2szRwyxbTPCygsNDj/2ZFCqVOQdeuZtaeQikRERERERERqZHDYTZBj4uDc8+t/PXiYsjKqhxW7d1rZlCFhkJYmLks2073vjK3CwMCWLR4MUM8/10pR6HUKXjiCdNERERERERERM5IYKBZute6NVxyiWf/7cJCz+5lVY0Ab3dARERERERERETsR6GUiIiIiIiIiIh4nEIpERERERERERHxOIVSIiIiIiIiIiLicQqlRERERERERETE4xRKiYiIiIiIiIiIxymUEhERERERERERj1MoJSIiIiIiIiIiHqdQSkREREREREREPE6hlIiIiIiIiIiIeJxCKRERERERERER8bggb3egLlmWBUBOTo6Xe+J5hYWF5Ofnk5OTQ3BwsLe741F2rh3sXb+dawfVb+f67Vw7uL9+5zjCOa6wG7uOp/R7pfrtWr+dawfVb+f67Vw7+M54ql6FUrm5uQAkJiZ6uSciIiLi73Jzc2nUqJG3u+FxGk+JiIhIXTnZeMph1aOPAUtKSti1axeRkZE4HA5vd8ejcnJySExMJDMzk6ioKG93x6PsXDvYu3471w6q387127l2cH/9lmWRm5tLixYtCAiw304Hdh1P6fdK9du1fjvXDqrfzvXbuXbwnfFUvZopFRAQQEJCgre74VVRUVG2/IUCe9cO9q7fzrWD6rdz/XauHdxbvx1nSDnZfTyl3yvVb9f67Vw7qH4712/n2sH74yn7ffwnIiIiIiIiIiJep1BKREREREREREQ8TqFUPREaGsrDDz9MaGiot7vicXauHexdv51rB9Vv5/rtXDuofnEPu/9cqX771m/n2kH127l+O9cOvlN/vdroXERERERERERE/INmSomIiIiIiIiIiMcplBIREREREREREY9TKOUHPv74Y9q0aUNQUBDdunVj48aNJ33OiBEjcDgcpW3AgAEe6KnUtZSUlHLvo7OlpKTU+LwuXbqUe/y4ceM802E5Y/v37yc5OZlt27aV3nc6xwDQccAfVXz/T/cYADoOiFSk8ZR9aTxlPxpP2ZfGUn7IEp+2ZcsWKyYmxnr33XetPXv2WL/97W+tCy644KTPa968ubVu3Trr0KFD1qFDh6y8vDwP9NY9JkyYYAGlrW3btid9zpIlS6yOHTtacXFx1tNPP+2BXrpHQUFB6Xt46NAhKzMz02rcuLG1ZcuWap9z9OhRKyIiwtq3b1/p8/Lz8z3Y6zOXnZ1tJSUlWRkZGaX3rVu3zjr33HOt6Oho67777rNKSkpq9Vrvv/++1apVK6t58+bWnDlz3NTjupGdnW317t3bAkprP91jgGX553Ggqvf+dI4BluV/x4Gq3v/TOQZYln8eB+bNm2clJydbgYGBVteuXa20tDTLsuzxuy/up/GUxlMaTxl2OKZqPGXf8ZTGUv45llIo5eM++eQT61//+lfp7a+++soKDw+v8TlZWVlWs2bN3N01jzn//POt+fPnlx4McnJyanz8vn37rKioKGvq1KlWenq61aNHD+urr77yUG/d6/HHH7duueWWGh+zdOlSq0+fPh7qUd2r6j+T48ePW0lJSdatt95qbdmyxRoyZIj1+uuvn/S11q1bZ4WEhFgzZsyw1q5da7Vr187atGmTmys4ff3797eef/75crWfzjHAsvzzOFDVe29Zp34MsCz/PA5U9f5XVJtjgGX533Gguj8W7PK7L+6n8ZTGU2VpPFW/j6kaT9l3PKWxlH+OpRRK+ZlXXnnF6tKlS42P+fDDD60mTZpYLVu2tCIiIqzrr7/eOnjwoId6WLcKCwutqKgoKzc3t9bPefbZZ62OHTuWJsDz5s2zxowZ464uesyxY8es+Pj4ag+wTs8884yVkJBgNW7c2GrUqJF12223WcePH/dMJ+tAVf+ZfPTRR1ZMTIx19OhRy7Isa/Xq1daFF1540te66667rEGDBpXefu6556y//OUvbul3Xfjll18sy7Jq/I+0NscAy/LP40BV7/3pHAMsyz+PAyd7/2t7DLAs/zsOVPfHgl1+98XzNJ46OX88jtaGxlP1/5iq8ZR9x1MaS/nnWEp7SvmREydO8PTTT3PbbbfV+LhNmzbRtWtX5s+fz/fff09GRgZTpkzxUC/r1rp16ygpKaFbt26Eh4dzxRVXsGPHjhqfs2bNGi699FIcDgcA5513HitXrvREd91qzpw59O7dm6SkpBoft3nzZi666CKWLl3KggULWLhwIc8++6xnOlkHZsyYwcSJE8vdt2bNGvr06UNERARg1nenpaWd9LXWrFnDZZddVnrb138WkpOTa/x6bY8B4J/Hgare+9M5BoB/HgdO9v7X9hgA/nccGDZsGH/84x9Lb2/evJn27dvb5ndfPEvjKY2nNJ6q38dUjafsO57SWMpPx1JuibrELe6//36ra9eu1okTJ07ped98840VFxfnpl6519tvv2317NnTWrZsmZWenm4NHTq0XGJblWuuucZ66qmnSm/n5eVZUVFR7u6q2/Xq1cuaP3/+KT9v1qxZVs+ePd3QI/eizCcc99xzj3X77beX+3rjxo1P+klVjx49rPfee6/09vr162v1qZi3Uc2nO6d7DLAs/zoOlK3/dI4BluXfx4Hq3v/TPQZYln8dBwoKCqx27dpZL7/8su1+98UzNJ7SeErjKZf6fEzVeMq+4ymNpfxrLBXknqhL6tpXX33FSy+9xPfff09wcPApPTc+Pp4DBw5QUFBAaGiom3roHmPGjGHMmDGlt19++WWSk5PJyckhKiqqyucEBQWVqzMsLIz8/Hy399WdtmzZwpYtWxg4cOApPzc+Pp6dO3e6oVeeU/E9Bdf7GhMTU+vn+fPPwpkcA8B/jwOncwyA+vXew5kdA8C/jgMPP/wwDRo0YNy4cTz44IO2/92XuqXxlKHxlMZTTnY7pmo8ZdhxPKWxlIuv/d5r+Z4fyMjIYNSoUbz00kt06tTppI+//vrrWbp0aent5cuX07RpU786cFYnPj6ekpISdu/eXe1jYmNjyc7OLr2dm5tLSEiIJ7rnNu+99x7Dhg2r1X+e559/PpmZmaW3ly9fTuvWrd3ZPber+J5C7d7X+vKzcKrHAKi/x4HaHAOg/rz3TqdyDAD/PQ44/1iYM2cOwcHBtv/dl7ql8ZSLxlMaTznZ6Ziq8ZSLHcdTGku5+NrvvUIpH3fs2DGGDRvGlVdeydVXX01eXh55eXlYlkVOTg6FhYWVntO5c2cmTZrE0qVLmTdvHlOmTGH8+PFe6P2Zmzx5MnPmzCm9vXz5cgICAkhMTKz2Ob169WL58uWlt1NTU2nZsqVb++luX3zxBZdcckm5+w4fPkxxcXGlx55zzjnceuut/PDDD8yaNYunn37ab99/p4rvaUZGBgUFBcTGxp7S8/zxZ6GmYwBQ748Dp3MMgPrx3pdV1TEA6tdxoKo/Fuz8uy91S+MpjadA4yk7H1M1ntJ4SmMp12N87vfeLYsCpc7MmzfPAiq1jIwMq3Xr1tZHH31U6TknTpyw/vCHP1gNGjSwmjVrZk2dOtUqLCz0fOfrwFtvvWUlJydbixYtshYsWGB16NDBGjt2rGVZlnXkyJEq14JnZ2dbYWFh1sKFC60TJ05YV1xxhXXnnXd6uut1Jj8/3woJCbE2btxY7n7ASk1NrfT4Q4cOWVdddZUVHh5utW7d2nr55Zc91NO6RYUzhjRp0qT09KXjxo2zhg0bVvrYQ4cOWUVFRZVeY/Xq1VaDBg2stWvXWrm5uVa3bt2sf/zjHx7p/5koW3tNxwDLsurlcaBsfTUdAyyrfh4HqLAPQnXHAOdj68NxID8/3+rUqZN1yy23WLm5uaXtxIkTtvrdF/fReErjKY2nNJ7SeMo+4ymNpfxrLKVQSnze/fffbzVq1MiKjY21Jk6caOXl5VmWVf1/HpZlTvMaHBxsxcTEWMnJydaePXs82GOpCxX/M/n444+tiIgIKy4uzmrSpIm1YcOGco+t6j8Ty7KsBx54wAoJCbGioqKsnj17Wvn5+W7uuZypiu99dccAy9JxoL6o6Y8F/e6L1A2Np+xJ4yn70njKXvx5LOX4tSMi9U5GRgabNm2ib9++NGzY0NvdkTqwZ88eVq5cSZ8+fYiLi6v189LS0ti5cyf9+vXz23Xwcnp0HKgf9Lsv4j06jtY/OqbKqdJxwP/58u+9QikREREREREREfE4bXQuIiIiIiIiIiIep1BKREREREREREQ8TqGUiIiIiIiIiIh4nEIpERERERERERHxOIVSIiIiIiIiIiLicQqlRMSvLFmyBIfDUa6569S0KSkpXHLJJW55bRERERFv0XhKRHxFkLc7ICJyqqKioti+fXvpbYfD4cXeiIiIiPgfjadExBcolBIRv+NwOIiOjvZ2N0RERET8lsZTIuILtHxPROqFRx55hMGDB9OvXz8aNWrEyJEjycnJKf36t99+S7du3YiJiWH06NEcPny49GuLFy+mS5cuREZGMnjwYLKyssq99owZM2jatClNmzblww8/9FRJIiIiIh6l8ZSIeJpCKRHxO0eOHCE6Orq03X777QB88cUX3HzzzaxYsYJt27bx0EMPAZCZmcmQIUO44447WLlyJXl5eYwdOxaAjIwMhg8fzt13301aWhpRUVHceeedpf/W+vXr+fDDD/nf//7HTTfdxN133+3pckVERETqnMZTIuILHJZlWd7uhIhIbS1ZsoQRI0awdu3a0vsaNmzIP//5TxYtWsTSpUsB+Oijj5g0aRLbtm1j+vTpfP3113z55ZcA7Ny5k4SEBHbv3s3rr7/ON998w4IFCwDIyspi9erVDBs2jJSUFMaPH8/27duJj48nPT2ds846Cx02RURExJ9pPCUivkJ7SomI3wkICCApKanS/YmJiaXXW7Zsyd69ewHzyV6bNm3KfS00NJQdO3ZU+lpCQgIJCQmlt88++2zi4+MBCAkJqetSRERERLxC4ykR8QVavici9ca2bdtKr2dmZtKsWTMAWrVqxS+//FL6tV27dlFQUEDr1q1JTEws97z09HS6d+9OSUkJYM5MIyIiImIXGk+JiCcplBIRv2NZFocPHy7XiouL+f7775k1axY///wzTz75JNdeey0AY8aMYdmyZcyYMYOMjAzGjx/PVVddRdOmTRk1ahTffvstKSkpZGZm8thjjxEfH09AgA6PIiIiUn9pPCUivkBHCRHxOzk5OcTExJRrP/30E8OHD2fmzJn06NGDtm3b8vDDDwNmGvr8+fN56aWX6N69OxEREbzxxhsAJCcn8/HHH/PMM89wzjnncPjw4dKviYiIiNRXGk+JiC/QRuciUi888sgjbNu2jZSUFG93RURERMQvaTwlIp6mmVIiIiIiIiIiIuJxmiklIiIiIiIiIiIep5lSIiIiIiIiIiLicQqlRERERERERETE4xRKiYiIiIiIiIiIxymUEhERERERERERj1MoJSIiIiIiIiIiHqdQSkREREREREREPE6hlIiIiIiIiIiIeJxCKRERERERERER8TiFUiIiIiIiIiIi4nH/D5IRJ8TGX2ogAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 1200x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "训练完成！最终测试准确率: 80.98%\n"
     ]
    }
   ],
   "source": [
    "# 5. 训练模型（记录每个 iteration 的损失）\n",
    "def train(model, train_loader, test_loader, criterion, optimizer, scheduler, device, epochs):\n",
    "    model.train()  # 设置为训练模式\n",
    "    \n",
    "    # 记录每个 iteration 的损失\n",
    "    all_iter_losses = []  # 存储所有 batch 的损失\n",
    "    iter_indices = []     # 存储 iteration 序号\n",
    "    \n",
    "    # 记录每个 epoch 的准确率和损失\n",
    "    train_acc_history = []\n",
    "    test_acc_history = []\n",
    "    train_loss_history = []\n",
    "    test_loss_history = []\n",
    "    \n",
    "    for epoch in range(epochs):\n",
    "        running_loss = 0.0\n",
    "        correct = 0\n",
    "        total = 0\n",
    "        \n",
    "        for batch_idx, (data, target) in enumerate(train_loader):\n",
    "            data, target = data.to(device), target.to(device)  # 移至GPU\n",
    "            \n",
    "            optimizer.zero_grad()  # 梯度清零\n",
    "            output = model(data)  # 前向传播\n",
    "            loss = criterion(output, target)  # 计算损失\n",
    "            loss.backward()  # 反向传播\n",
    "            optimizer.step()  # 更新参数\n",
    "            \n",
    "            # 记录当前 iteration 的损失\n",
    "            iter_loss = loss.item()\n",
    "            all_iter_losses.append(iter_loss)\n",
    "            iter_indices.append(epoch * len(train_loader) + batch_idx + 1)\n",
    "            \n",
    "            # 统计准确率和损失\n",
    "            running_loss += iter_loss\n",
    "            _, predicted = output.max(1)\n",
    "            total += target.size(0)\n",
    "            correct += predicted.eq(target).sum().item()\n",
    "            \n",
    "            # 每100个批次打印一次训练信息\n",
    "            if (batch_idx + 1) % 100 == 0:\n",
    "                print(f'Epoch: {epoch+1}/{epochs} | Batch: {batch_idx+1}/{len(train_loader)} '\n",
    "                      f'| 单Batch损失: {iter_loss:.4f} | 累计平均损失: {running_loss/(batch_idx+1):.4f}')\n",
    "        \n",
    "        # 计算当前epoch的平均训练损失和准确率\n",
    "        epoch_train_loss = running_loss / len(train_loader)\n",
    "        epoch_train_acc = 100. * correct / total\n",
    "        train_acc_history.append(epoch_train_acc)\n",
    "        train_loss_history.append(epoch_train_loss)\n",
    "        \n",
    "        # 测试阶段\n",
    "        model.eval()  # 设置为评估模式\n",
    "        test_loss = 0\n",
    "        correct_test = 0\n",
    "        total_test = 0\n",
    "        \n",
    "        with torch.no_grad():\n",
    "            for data, target in test_loader:\n",
    "                data, target = data.to(device), target.to(device)\n",
    "                output = model(data)\n",
    "                test_loss += criterion(output, target).item()\n",
    "                _, predicted = output.max(1)\n",
    "                total_test += target.size(0)\n",
    "                correct_test += predicted.eq(target).sum().item()\n",
    "        \n",
    "        epoch_test_loss = test_loss / len(test_loader)\n",
    "        epoch_test_acc = 100. * correct_test / total_test\n",
    "        test_acc_history.append(epoch_test_acc)\n",
    "        test_loss_history.append(epoch_test_loss)\n",
    "        \n",
    "        # 更新学习率调度器\n",
    "        scheduler.step(epoch_test_loss)\n",
    "        \n",
    "        print(f'Epoch {epoch+1}/{epochs} 完成 | 训练准确率: {epoch_train_acc:.2f}% | 测试准确率: {epoch_test_acc:.2f}%')\n",
    "    \n",
    "    # 绘制所有 iteration 的损失曲线\n",
    "    plot_iter_losses(all_iter_losses, iter_indices)\n",
    "    \n",
    "    # 绘制每个 epoch 的准确率和损失曲线\n",
    "    plot_epoch_metrics(train_acc_history, test_acc_history, train_loss_history, test_loss_history)\n",
    "    \n",
    "    return epoch_test_acc  # 返回最终测试准确率\n",
    "\n",
    "# 6. 绘制每个 iteration 的损失曲线\n",
    "def plot_iter_losses(losses, indices):\n",
    "    plt.figure(figsize=(10, 4))\n",
    "    plt.plot(indices, losses, 'b-', alpha=0.7, label='Iteration Loss')\n",
    "    plt.xlabel('Iteration（Batch序号）')\n",
    "    plt.ylabel('损失值')\n",
    "    plt.title('每个 Iteration 的训练损失')\n",
    "    plt.legend()\n",
    "    plt.grid(True)\n",
    "    plt.tight_layout()\n",
    "    plt.show()\n",
    "\n",
    "# 7. 绘制每个 epoch 的准确率和损失曲线\n",
    "def plot_epoch_metrics(train_acc, test_acc, train_loss, test_loss):\n",
    "    epochs = range(1, len(train_acc) + 1)\n",
    "    \n",
    "    plt.figure(figsize=(12, 4))\n",
    "    \n",
    "    # 绘制准确率曲线\n",
    "    plt.subplot(1, 2, 1)\n",
    "    plt.plot(epochs, train_acc, 'b-', label='训练准确率')\n",
    "    plt.plot(epochs, test_acc, 'r-', label='测试准确率')\n",
    "    plt.xlabel('Epoch')\n",
    "    plt.ylabel('准确率 (%)')\n",
    "    plt.title('训练和测试准确率')\n",
    "    plt.legend()\n",
    "    plt.grid(True)\n",
    "    \n",
    "    # 绘制损失曲线\n",
    "    plt.subplot(1, 2, 2)\n",
    "    plt.plot(epochs, train_loss, 'b-', label='训练损失')\n",
    "    plt.plot(epochs, test_loss, 'r-', label='测试损失')\n",
    "    plt.xlabel('Epoch')\n",
    "    plt.ylabel('损失值')\n",
    "    plt.title('训练和测试损失')\n",
    "    plt.legend()\n",
    "    plt.grid(True)\n",
    "    \n",
    "    plt.tight_layout()\n",
    "    plt.show()\n",
    "\n",
    "# 8. 执行训练和测试\n",
    "epochs = 20  # 增加训练轮次以获得更好效果\n",
    "print(\"开始使用CNN训练模型...\")\n",
    "final_accuracy = train(model, train_loader, test_loader, criterion, optimizer, scheduler, device, epochs)\n",
    "print(f\"训练完成！最终测试准确率: {final_accuracy:.2f}%\")\n",
    "\n",
    "# # 保存模型\n",
    "# torch.save(model.state_dict(), 'cifar10_cnn_model.pth')\n",
    "# print(\"模型已保存为: cifar10_cnn_model.pth\")"
   ]
  },
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   "cell_type": "markdown",
   "id": "a3b023e1",
   "metadata": {},
   "source": [
    "以CIFAR-10为例，假设两者均使用2层隐藏层：\n",
    "| 模型结构         | 参数规模       | 特征提取方式       | 计算效率       | 典型准确率   |\n",
    "|------------------|----------------|--------------------|----------------|--------------|\n",
    "| **MLP**          | 3072→1024→512→10<br>≈370万参数 | 全连接，无空间感知 | 每次计算需遍历所有参数 | 50-55%       |\n",
    "| **CNN**（简单）  | 3×3卷积→池化→全连接<br>≈10万参数 | 局部感知+权值共享 | 卷积核复用计算，效率高 | 70-80%       |\n",
    "\n",
    "\n"
   ]
  }
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